Publications


Pre-Prints

2020

Zhang, Kai, Vitor Albiero, Kevin W. Bowyer; This paper will appear at IWBF2020.

[arxiv]

Aidan Boyd,Adam Czajka, Kevin Bowyer, The IEEE/IAPR International Join Conference on Biometrics (IJCB 2020), Sept. 28 – Oct. 1, 2020, Houston, USA.

[arxiv]

2019

Vítor Albiero, Nisha Srinivas, Esteban Villalobos, Jorge Perez-Facuse, Roberto Rosenthal, Domingo Mery, Karl Ricanek, Kevin W Bowyer, December 2019.

[arxiv]

KS Krishnapriya, Kushal Vangara, Michael C King, Vitor Albiero, Kevin Bowyer; 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA, 2019, pp. 2278-2285.

[arxiv] [pdf]

@InProceedings{S_2019_CVPR_Workshops,
author = {S, Krishnapriya K. and Vangara, Kushal and King, Michael C. and Albiero, Vitor and Bowyer, Kevin},
title = {Characterizing the Variability in Face Recognition Accuracy Relative to Race},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}




Joel Brogan, Aparna Bharati, Daniel Moreira, Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter Scheirer.

[arxiv]


2018

Nathaniel Blanchard, Jeffery Kinnison, Brandon RichardWebster, Pouya BashivanWalter J. ScheirerMay 2018.

Funded by: IARPA contract #D16PC00002 and NSF DGE #1313583

[arxiv] [pdf]

@article{blanchard2018,

  author    = {Nathaniel Blanchard and

                Jeffery Kinnison and

                Brandon RichardWebster and

                Pouya Bashivan and

                Walter J. Scheirer},

  title     = {A Neurobiological Cross-domain Evaluation Metric for Predictive Coding Networks},

  journal   = {},

  volume    = {},

  year      = {2018},

  url       = {},

  timestamp = {},

  biburl    = {},

  bibsource = {}

}

Pei Li, Loreto Prieto, Domingo Mery, Patrick FlynnMay 2018.

[arxiv] [pdf]

@article{DBLP:journals/corr/abs-1805-11519,

  author    = {Pei Li and

               Loreto Prieto and

               Domingo Mery and

               Patrick J. Flynn},

  title     = {Face Recognition in Low Quality Images: {A} Survey},

  journal   = {CoRR},

  volume    = {abs/1805.11519},

  year      = {2018},

  url       = {http://arxiv.org/abs/1805.11519},

  archivePrefix = {arXiv},

  eprint    = {1805.11519},

  timestamp = {Wed, 06 Jun 2018 14:48:18 +0200},

  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1805-11519},

  bibsource = {dblp computer science bibliography, https://dblp.org}

}

Ali Shahbazi, Jeffery Kinnison, Rafael Vescovi, Ming Du, Robert Hill, Maximilian Jösch, Marc Takeno, Hongkui Zeng, Nuno Maçarico da Costa, Jaime Grutzendler, Narayanan Kasthuri, Walter J. Scheirer, March 2018.

[pdf] [supp. material]

@article {Shahbazi278515,

author = {Shahbazi, Ali and Kinnison, Jeffery and Vescovi, Rafael and Du, Ming and Hill, Robert and J{\"o}sch, Maximilian and Takeno, Marc and Zeng, Hongkui and da Costa, Nuno Ma{\c c}arico and Grutzendler, Jaime and Kasthuri, Narayanan and Scheirer, Walter J.},

title = {Flexible Learning-Free Segmentation and Reconstruction for Sparse Neuronal Circuit Tracing},

year = {2018},

doi = {10.1101/278515},

publisher = {Cold Spring Harbor Laboratory},

abstract = {Imaging is a dominant strategy for data collection in neuroscience, yielding 3D stacks of images that can scale to petabytes of data for a single experiment. Machine learning-based algorithms from the computer vision domain can serve as a pair of virtual eyes that tirelessly processes these images to automatically construct more complete and realistic circuits. In practice, such algorithms are often too error-prone and computationally expensive to be immediately useful. Therefore we introduce a new fast and flexible learning-free automated method for sparse segmentation and 2D/3D reconstruction of brain micro-structures. Unlike supervised learning methods, our pipeline exploits structure-specific contextual clues and requires no extensive pre-training. This approach generalizes across different modalities and sample targets, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. Experiments on newly published and novel mouse datasets demonstrate the high biological fidelity and recall of the proposed pipeline, as well as reconstructions of sufficient quality for preliminary biological study. Compared to existing supervised methods, it is both significantly faster (up to several orders of magnitude) and produces high-quality reconstructions that are robust to noise and artifacts.},

URL = {https://www.biorxiv.org/content/early/2018/03/08/278515},

eprint = {https://www.biorxiv.org/content/early/2018/03/08/278515.full.pdf},

journal = {bioRxiv}

}

2016

Kevin W Bowyer, Patrick J Flynn, June 2016.

[arxiv] [pdf]

@article{DBLP:journals/corr/BowyerF16,

  author    = {Kevin W. Bowyer and

               Patrick J. Flynn},

  title     = {The {ND-IRIS-0405} Iris Image Dataset},

  journal   = {CoRR},

  volume    = {abs/1606.04853},

  year      = {2016},

  url       = {http://arxiv.org/abs/1606.04853},

  archivePrefix = {arXiv},

  eprint    = {1606.04853},

  timestamp = {Wed, 07 Jun 2017 14:42:19 +0200},

  biburl    = {https://dblp.org/rec/bib/journals/corr/BowyerF16},

  bibsource = {dblp computer science bibliography, https://dblp.org}

}

Jason Grant, Patrick Flynn, May 2016.

@article{DBLP:journals/corr/GrantF16,

  author    = {Jason M. Grant and

               Patrick J. Flynn},

  title     = {Hierarchical Clustering in Face Similarity Score Space},

  journal   = {CoRR},

  volume    = {abs/1605.06052},

  year      = {2016},

  url       = {http://arxiv.org/abs/1605.06052},

  archivePrefix = {arXiv},

  eprint    = {1605.06052},

  timestamp = {Wed, 07 Jun 2017 14:40:53 +0200},

  biburl    = {https://dblp.org/rec/bib/journals/corr/GrantF16},

  bibsource = {dblp computer science bibliography, https://dblp.org}

}


Published

2021

Jeremy Speth, Nathan Vance, Patrick Flynn, Kevin Bowyer, Adam Czajka, Computer Vision and Image Understanding, Volume 210, 2021,

DOI: 10.1016/j.cviu.2021.103246

@article{SPETH2021103246,
title = {Unifying frame rate and temporal dilations for improved remote pulse detection},
journal = {Computer Vision and Image Understanding},
volume = {210},
pages = {103246},
year = {2021},
issn = {1077-3142},
doi = {https://doi.org/10.1016/j.cviu.2021.103246},
url = {https://www.sciencedirect.com/science/article/pii/S1077314221000904},
author = {Jeremy Speth and Nathan Vance and Patrick Flynn and Kevin Bowyer and Adam Czajka},
keywords = {Remote photoplethysmography, Face video, Physiological monitoring, Spatiotemporal modeling, Pulse detection},
abstract = {Remote photoplethysmography (rPPG) is the monitoring of blood volume pulse from a camera at a distance. 3-Dimensional Convolutional Neural Networks (3DCNNs) have shown promising performance on the rPPG task, although it is critical that we understand the impact of both video and model parameters. In this paper, we explore the effect of frame rate, temporal kernel width, and – more generally – temporal receptive field on the reliability of heart rate and waveform estimation carried out by 3DCNNs. We train and evaluate 32 3DCNNs with different temporal parameters on a new large-scale database for physiological monitoring in an interview scenario. We show that previous studies reporting null effects of frame rate changes on pulse estimators may no longer be valid when using CNNs, and decreasing the frame rate may actually improve performance. In particular, we found that models trained on videos with frame rates as low as 12.9 frames per second (fps) perform better than those trained on videos recorded at a full 90 fps, perhaps due to the temporal receptive fields becoming larger in time dimension when the fps decreases. Using this insight, we propose RemotePulseNet, a novel 3DCNN architecture that exploits temporally dilated convolutions with increasing dilation rate to drastically increase the receptive field. We compare its performance with that of recent state-of-the-art pulse estimation methods, and show that both RemotePulseNet and the low frame rate 3DCNNs produce high-quality pulse signals from faces captured under a challenging interview scenario. The source code and instructions for obtaining a copy of the test data are made available with this paper.}
}

Derek Prijatelj, Samuel Grieggs, Futoshi Yumoto, Eric Robertson, Walter J. Scheirer,
International Conference on Document Analysis and Recognition (ICDAR),
September 2021.

[arxiv] [pdf]

@inproceedings{Prijatelj_ICDAR2021,
author = {Derek Prijatelj and
Samuel Grieggs and
Futoshi Yumoto and
Eric Robertson and
Walter J. Scheirer},
title = {Handwriting Recognition with Novelty},
booktitle = {International Conference on Document Analysis and Recognition (ICDAR},
year = {2021}
}


William Theisen, Joel Brogan, Pamela Bilo Thomas, Daniel Moreira, Pascal Phoa, Tim Weninger, Walter Scheirer, Proceedings of the International AAAI Conference on Web and Social Media (ICWSM),
June 2021.

[arxiv] [pdf] [code] [data]

@inproceedings{theisen2021automatic,
title={Automatic Discovery of Political Meme Genres with Diverse Appearances},
author={William Theisen and Joel Brogan and Pamela Bilo Thomas and
Daniel Moreira and Pascal Phoa and Tim Weninger and Walter Scheirer},
booktitle={International AAAI Conference on Web and Social Media (ICWSM)}
year={2021},
}


Z. Fang, A. Czajka and K. W. Bowyer, IEEE Transactions on Information Forensics and Security, vol. 16, pp. 510-520, 2021, doi: 10.1109/TIFS.2020.3015547.

DOI: 10.1109/TIFS.2020.3015547

[arxiv]

@ARTICLE{9164898,
  author={Fang, Zhaoyuan and Czajka, Adam and Bowyer, Kevin W.},
  journal={IEEE Transactions on Information Forensics and Security},
  title={Robust Iris Presentation Attack Detection Fusing 2D and 3D Information},
  year={2021},
  volume={16},
  number={},
  pages={510-520},
  doi={10.1109/TIFS.2020.3015547}}


Samuel Grieggs, Bingyu Shen, Greta Rauch, Pei Li, Jiaqi Ma, David Chiang,
Brian Price, Walter J. Scheirer,
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),
Accepted for Publicatiton in June 2021.

[arxiv] [pdf]

@article{DBLP:journals/corr/abs-1904-03734,
author = {Samuel Grieggs and
Bingyu Shen and
Greta Rauch and
Pei Li and
Jiaqi Ma and
David Chiang and
Brian Price and
Walter J. Scheirer},
title = {Measuring Human Perception to Improve Handwritten Document
Transcription},
journal = {CoRR},
volume = {abs/1904.03734},
year = {2020},
url = {http://arxiv.org/abs/1904.03734},
archivePrefix = {arXiv},
eprint = {1904.03734},
}


Michael Yankoski, Walter J. Scheirer, Tim Weninger, Bulletin of the Atomic Scientists, May 2021.

DOI: 10.1080/00963402.2021.1912093

[pdf]

Sophia Abraham, Zachariah Carmichael, Sreya Banerjee, Rosaura G. VidalMata, Ankit Agrawal, Md Nafee Al Islam, Walter J. Scheirer, Jane Cleland-Huang,Workshop on AI Engineering – Software Engineering for AI (WAIN), May 2021.

[arxiv] [pdf]

@inproceedings{Abraham_WAIN2021,
author = {Sophia Abraham and
Zachariah Carmichael and
Sreya Banerjee and
Rosaura G. VidalMata and
Ankit Agrawal and
Md Nafee Al Islam and Walter J. Scheirer and Jane Cleland-Huang},
title = {Adaptive Autonomy in Human-on-the-Loop Vision-Based Robotics Systems},
booktitle = {Workshop on AI Engineering – Software Engineering for AI (WAIN},
year = {2021}
}


Terrance Boult, Przemyslaw Grabowicz, Derek Prijatelj, Roni Stern, Lawrence Holder,
Joshua Alspector, Mohsen Jafarzadeh, Touqeer Ahmad, Akshay Dhamija, Chunchun Li,
Steve Cruz, Abhinav Shrivastava, Carl Vondrick, Walter J. Scheirer,
AAAI Conference on Artificial Intelligence (AAAI 2021), Senior Member Track,
February 2021.
[pdf]

@inproceedings{Boult21,
author = {Terrance Boult and
Przemyslaw Grabowicz and
Derek Prijatelj and
Lawrence Holder and
Joshua Alspector and
Mohsen Jafarzadeh and
Touqeer Ahmad and
Akshay Dhamija and
Chunchun Li and
Steve Cruz and
Abhinav Shrivastava and
Carl Vondrick and
Walter J. Scheirer},
title = {Towards a Unifying Framework for Formal Theories of Novelty},
booktitle = {AAAI Conference on Artificial Intelligence (AAAI 2021)},
year = {2021},
}


Sreya Banerjee, Lauren Alvey, Paula Brown, Sophie Yue, Lei Li, Walter J. Scheirer, Sci Rep 11, 1002 (2021).

DOI: 10.1038/s41598-020-79772-3

[code]

@article {Banerjee2020.09.01.277657,
author = {Banerjee, Sreya and Alvey, Lauren and Brown, Paula and Yue,
Sophie and Li, Lei and Scheirer, Walter J.},
title = {An Assistive Computer Vision Tool to Automatically Detect
Changes in Fish Behavior In Response to Ambient Odor},
elocation-id = {2020.09.01.277657},
year = {2020},
doi = {10.1101/2020.09.01.277657},
publisher = {Cold Spring Harbor Laboratory},
journal = {bioRxiv}

Bingyu Shen, Boyang Li, Walter J. Scheirer,Proceedings of the 1st Autonomous Vehicle Vision Workshop,January 2021.

[pdf]

@InProceedings{Shen_2021_WACV,
author = {Shen, Bingyu and Li, Boyang and Scheirer, Walter J.},
title = {Automatic Virtual 3D City Generation for Synthetic Data Collection},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications
of Computer Vision (WACV) Workshops},
month = {January},
year = {2021},
pages = {161-170}
}

2020

Kevin Bowyer, Michael King, Walter J. Scheirer, Kushal Vangara,IEEE Transactions on Technology and Society (T-TS),December 2020.

[arxiv] [pdf]

@article{bowyer2020criminality,
title={The Criminality From Face Illusion},
author={Kevin W. Bowyer and Michael King and Walter Scheirer and Kushal Vangara},
journal={IEEE Transactions on Technology and Society (T-TS)},
volume={1},
number={4},
month={December},
year={2020},
}

Stella Biderman, Walter J. Scheirer,Proceedings of the I Can't Believe It's Not Better! Workshop (ICBINB@NeurIPS 2020), December 2020.


[pdf] [poster]

@inproceedings{Biderman20_ICBINB,
author = {Stella Biderman and
Walter J. Scheirer},
title = {Pitfalls in Machine Learning Research: Reexamining the Development Cycle},
booktitle = {Proceedings of the I Can't Believe It's Not Better! Workshop
(ICBINB@NeurIPS 2020)},
year = {2020}
}

Kenton Murray, Jeff Kinnison, Toan Nguyen, Walter J. Scheirer, David Chiang; Proceedings of the Workshop on Neural Generation and Translation (WNGT), November 2019.

[pdf]

@InProceedings{MurrayWNGT19,
author = {Kenton Murray and
Jeff Kinnison and
Toan Nguyen and
Walter J. Scheirer and
David Chiang},
title = {Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance
for Low-Resource Machine Translation},
booktitle = {Workshop on Neural Generation and Translation (WNGT)},
year = {2019}
}

Aidan Boyd, Zhaoyuan Fang, Adam Czajka, Kevin W. Bowyer, Pattern Recognition Letters, Vol. 138, pp. 483–489.

DOI: 10.1016/j.patrec.2020.08.018

[arxiv]

@article{BOYD2020483,
title = {Iris presentation attack detection: Where are we now?},
journal = {Pattern Recognition Letters},
volume = {138},
pages = {483-489},
year = {2020},
issn = {0167-8655},
doi = {https://doi.org/10.1016/j.patrec.2020.08.018},
url = {https://www.sciencedirect.com/science/article/pii/S0167865520303226},
author = {Aidan Boyd and Zhaoyuan Fang and Adam Czajka and Kevin W. Bowyer},
keywords = {Biometrics, Iris presentation attack detection, Security},
abstract = {As the popularity of iris recognition systems increases, the importance of effective security measures against presentation attacks becomes paramount. This work presents an overview of the most important advances in the area of iris presentation attack detection published in the recent two years. Newly-released, publicly-available datasets for development and evaluation of iris presentation attack detection are discussed. Recent literature can be seen to be broken into three categories: traditional “hand-crafted” feature extraction and classification, deep learning-based solutions, and hybrid approaches fusing both methodologies. Conclusions of modern approaches underscore the difficulty of this task. Finally, commentary on possible directions for future research is provided.}
}

Jacob Dumford, Walter H. Sheirer; Proceedings of the IAPR/IEEE International Joint Conference on Biometrics (IJCB), September 2020.

[pdf] [talk]

@inproceedings{Dumford20_IJCB,
author = {Jacob Dumford and
Walter J. Scheirer},
title = {Backdooring Convolutional Neural Networks via Targeted Weight Perturbations},
booktitle = {Proceedings of the IAPR/IEEE International Joint Conference on Biometrics (IJCB)},
year = {2020}
}

Mel McCurrie, Hamish Nicholson, Walter J. Scheirer, Samuel E. Anthony,Proceedings of the IAPR/IEEE International Joint Conference on Biometrics (IJCB),September 2020.

[pdf]

@inproceedings{McCurrie20_IJCB,
author = {Mel McCurrie and
Hamish Nicholson and
Walter J. Scheirer and
Samuel E. Anthony},
title = {Modeling Score Distributions and Continuous Covariates:
A Bayesian Approach},
booktitle = {Proceedings of the IAPR/IEEE International Joint Conference on
Biometrics (IJCB)},
year = {2020}
}

Aidan Boyd, Shivanngi Yadav, Thomas Swearingen, Andrey Kuehlkamp, Mateusz Trokielewicz, Eric Benjamin, Piotr Maciejewicz, Dennis Chute, Arun Ross, Patrick Flynn, Kevin Bowyer, Adam Czajka, IEEE Access,  vol. 8, pp. 136570-136593, 2020.

DOI: 10.1109/ACCESS.2020.3011364

@ARTICLE{9146139,

  author={Boyd, Aidan and Yadav, Shivangi and Swearingen, Thomas and Kuehlkamp, Andrey and Trokielewicz, Mateusz and Benjamin, Eric and Maciejewicz, Piotr and Chute, Dennis and Ross, Arun and Flynn, Patrick and Bowyer, Kevin and Czajka, Adam},
  journal={IEEE Access},
  title={Post-Mortem Iris Recognition—A Survey and Assessment of the State of the Art},
  year={2020},
  volume={8},
  number={},
  pages={136570-136593},
  doi={10.1109/ACCESS.2020.3011364}}


Walter J. Scheirer, Bulletin of the Atomic Scientists,July 2020.

[pdf]

@article{scheirer2020pandemic,
title={A Pandemic of Bad Science},
author={Scheirer, Walter},
journal={Bulletin of the Atomic Scientists},
volume={76},
number={4},
month={July},
year={2020},
publisher={Taylor \& Francis}
}

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz, Journal of Forensic Sciences, Vol. 65, No. 5, pp. 1530-1538, 2020.

DOI: 10.1111/1556-4029.14488

[arxiv]


@article{https://doi.org/10.1111/1556-4029.14488,
author = {Trokielewicz, Mateusz and Czajka, Adam and Maciejewicz, Piotr},
title = {Post-mortem Iris Decomposition and its Dynamics in Morgue Conditions},
journal = {Journal of Forensic Sciences},
volume = {65},
number = {5},
pages = {1530-1538},
keywords = {iris recognition, eye, decomposition, postmortem, biometrics},
doi = {https://doi.org/10.1111/1556-4029.14488},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1556-4029.14488},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/1556-4029.14488},
abstract = {Abstract With increasing interest in employing iris biometrics as a forensic tool for identification by investigation authorities, there is a need for a thorough examination and understanding of postmortem decomposition processes that take place within the human eyeball, especially the iris. This can prove useful for fast and accurate matching of antemortem with postmortem data acquired at crime scenes or mass casualties, as well as for ensuring correct dispatching of bodies from the incident scene to a mortuary or funeral homes. Following these needs of forensic community, this paper offers an analysis of the coarse effects of eyeball decay done from a perspective of automatic iris recognition. We analyze postmortem iris images acquired for a subject with a very long postmortem observation time horizon (34 days), in both visible light and near-infrared light (860 nm), as the latter wavelength is used in commercial iris recognition systems. Conclusions and suggestions are provided that may aid forensic examiners in successfully utilizing iris patterns in postmortem identification of deceased subjects. Initial guidelines regarding the imaging process, types of illumination, and resolution are also given, together with expectations with respect to the iris features decomposition rates. Visible iris features possible for human, expert-based matching persists even up to 407 h postmortem, and near-infrared illumination is suggested for better mitigation of corneal opacity while imaging cadaver eyes (Post-mortem iris decomposition and its dynamics in morgue conditions. ArXiv pre-print, 2019).},
year = {2020}
}


Rosaura G VidalMata, Sreya Banerjee, Brandon RichardWebster, Michael Albright, Pedro Davalos, Scott McCloskey, Ben Miller, Asong Tambo, Sushobhan Ghosh, Sudarshan Nagesh, Ye Yuan, Yueyu Hu, Junru Wu, Wenhan Yang, Xiaoshuai Zhang, Jiaying Liu, Zhangyang Wang, Hwann-Tzong Chen, Tzu-Wei Huang, Wen-Chi Chin, Yi-Chun Li, Mahmoud Lababidi, Charles Otto, Walter J Scheirer, IEEE Transactions on Pattern Analysis and Machine Intelligence, May 21,2020.


DOI: 10.1109/TPAMI.2020.2996538

[arxiv]

@ARTICLE{9097964,

author={W. {Scheirer} and R. {VidalMata} and S. {Banerjee} and B. {RichardWebster} and M. {Albright} and P. {Davalos} and S. {McCloskey} and B. {Miller} and A. {Tambo} and S. {Ghosh} and S. {Nagesh} and Y. {Yuan} and Y. {Hu} and J. {Wu} and W. {Yang} and X. {Zhang} and J. {Liu} and Z. {Wang} and H. {Chen} and T. {Huang} and W. {Chin} and Y. {Li} and M. {Lababidi} and C. {Otto}},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Bridging the Gap Between Computational Photography and Visual Recognition},

year={2020},
volume={},
number={},
pages={1-1},}



Ankit Agrawal, Sophia J Abraham, Benjamin Burger, Chichi Christine, Luke Fraser, John M Hoeksema, Sarah Hwang, Elizabeth Travnik, Shreya Kumar, Walter Scheirer, Jane Cleland-Huang, Michael Vierhauser, Ryan Bauer, Steve Cox, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems,pp1 -13.

DOI: 10.1145/3313831.3376825

[pdf]

Ankit Agrawal, Sophia Abraham, Benjamin Burger, Chichi Christine, Luke Fraser,John Hoeksema, Sara Hwang, Elizabeth Travnik, Shreya Kumar, Walter J. Scheirer,Jane Cleland-Huang, Michael Vierhauser,Ryan Bauer, Steve Cox,Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (Honorable Mention Award), April 2020.

[pdf]

@misc{agrawal2020generation,
title={The Next Generation of Human-Drone Partnerships: Co-Designing an Emergency
Response System},
author={Ankit Agrawal and Sophia Abraham and Benjamin Burger and Chichi Christine
and Luke Fraser and John Hoeksema and Sara Hwang and Elizabeth Travnik and
Shreya Kumar and Walter Scheirer and Jane Cleland-Huang and Michael Vierhauser
and Ryan Bauer and Steve Cox},
year={2020},
eprint={2001.03849},
archivePrefix={arXiv},
primaryClass={cs.HC}
}


Wenhan Yang, Ye Yuan, Wenqi Ren, Jiaying Liu, Walter J Scheirer, Zhangyang Wang, Taiheng Zhang, Qiaoyong Zhong, Di Xie, Shiliang Pu, Yuqiang Zheng, Yanyun Qu, Yuhong Xie, Liang Chen, Zhonghao Li, Chen Hong, Hao Jiang, Siyuan Yang, Yan Liu, Xiaochao Qu, Pengfei Wan, Shuai Zheng, Minhui Zhong, Taiyi Su, Lingzhi He, Yandong Guo, Yao Zhao, Zhenfeng Zhu, Jinxiu Liang, Jingwen Wang, Tianyi Chen, Yuhui Quan, Yong Xu, Bo Liu, Xin Liu, Qi Sun, Tingyu Lin, Xiaochuan Li, Feng Lu, Lin Gu, Shengdi Zhou, Cong Cao, Shifeng Zhang, Cheng Chi, Chubing Zhuang, Zhen Lei, Stan Z Li, Shizheng Wang, Ruizhe Liu, Dong Yi, Zheming Zuo, Jianning Chi, Huan Wang, Kai Wang, Yixiu Liu, Xingyu Gao, Zhenyu Chen, Chang Guo, Yongzhou Li, Huicai Zhong, Jing Huang, Heng Guo, Jianfei Yang, Wenjuan Liao, Jiangang Yang, Liguo Zhou, Mingyue Feng, Likun Qin, IEEE Transactions on Image Processing,vol.29,pp5737-5752, 2020.

DOI: 10.1109/TIP.2020.2981922

[pdf]

@article{yang2020advancing,
title={Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study},
author={Yang, Wenhan and
Yuan, Ye and
Ren, Wenqi and
Liu, Jiaying and
Scheirer, Walter and
Wang, Zhangyang and
others},
journal={IEEE Transactions on Image Processing},
volume={29},
pages={5737--5752},
year={2020},
publisher={IEEE}
}

Mateusz Trokielewicz,Adam Czajka, Piotr Maciejewicz, The IEEE Winter Conf. on Applications of Computer Vision, Snowmass Village, Colorado, March 2-5, 2020.

[arxiv]

K.S. Krishnapriya, Vitor Albiero, Kushal Vangara, Michael C. King, Kevin W. Bowyer, IEEE Transactions on Technology and Society, vol. 1, no. 1, pp. 8-20, March 2020.



Sandipan Banerjee, Walter J. Scheirer, Kevin W. Bowyer, Patrick J. Flynn;The IEEE Winter Conference on Applications of Computer Vision,p.300-309

[pdf] [arxiv]

@inproceedings{BanerjeeWACV2020,
author = {Sandipan Banerjee and
Walter J. Scheirer and
Kevin W. Bowyer and
Patrick J. Flynn},
title = {On Hallucinating Context and Background Pixels from a Face Mask using
Multi-scale GANs},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision
(WACV)},
year = {2020}
}

Michael Yankoski, Tim Weninger, Walter J. Scheirer, Bulletin of the Atomic Scientists, March 2020.

DOI: 10.1080/00963402.2020.1728976

[pdf]

@article{yankoski2020ai,
title={An AI early warning system to monitor online disinformation, stop violence, and protect elections},
author={Yankoski, Michael and Weninger, Tim and Scheirer, Walter},
journal={Bulletin of the Atomic Scientists},
month={March},
year={2020},
publisher={Taylor \& Francis}
}

Mateusz Trokielewicz,Adam Czajka, Piotr Maciejewicz,
Image and Vision Computing, Vol. 94 (103866), Feb. 2020, pp. 1-11.

DOI: 10.1016/j.imavis.2019.103866

[arxiv]

Michael Milford, Samuel E. Anthony, Walter J. Scheirer, IEEE Potentials, January-February 2020.

[pdf]

@article{MilfordPotentials2020,
author = {Michael Milford and
Samuel E. Anthony and
Walter J. Scheirer},
title = {Self-Driving Vehicles: Key Technical Challenges and Progress Off the Road},
journal = {IEEE Potentials},
volume = {39},
number = {1},
month = {January-February},
year = {2020}
}

R. G. VidalMata, W. J. Scheirer, A. Kukleva, D.D. Cox and H. Kuehne, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)

[arxiv]

@InProceedings{vidalmata2021joint,
title={Joint Visual-Temporal Embedding for Unsupervised Learning of Actions
in Untrimmed Sequences},
author={Rosaura G. VidalMata and Walter J. Scheirer and David D. Cox
and Anna Kukleva and Hilde Kuehne},
booktitle={IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
year={2021},
}

Vitor Albiero, Krishnapriya KS, Kushal Vangara, Kai Zhang, Michael C King, Kevin W Bowyer;The IEEE Winter Conference on Applications of Computer Vision (WACV) Workshops, 2020, pp. 81-8.

[pdf]

@InProceedings{Albiero_2020_WACV,
author = {Albiero, Vitor and K.S., Krishnapriya and Vangara, Kushal and Zhang, Kai and King, Michael C. and Bowyer, Kevin W.},
title = {Analysis of Gender Inequality In Face Recognition Accuracy},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV) Workshops},
month = {March},
year = {2020}
}

Aparna Bharati, Daniel Moreira, Patrick Flynn, Anderson Rocha, Kevin Bowyer, Walter Scheirer. IEEE Transactions on Information Forensics and Security (T-IFS), Accepted for Publication in 2020.

[arxiv] [pdf] [code]

@misc{bharati2020learning,
title={Learning Transformation-Aware Embeddings for Image Forensics},
author={Aparna Bharati and Daniel Moreira and Patrick Flynn and
Anderson Rocha and Kevin Bowyer and Walter Scheirer},
year={2020},
eprint={2001.04547},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

Vitor Albiero, Kevin Bowyer, Kushal Vangara, Michael King; The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 261-269.

[pdf]

@InProceedings{Albiero_2020_WACV,
author = {Albiero, Vitor and Bowyer, Kevin and Vangara, Kushal and King, Michael},
title = {Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}

2019

Klemen Grm, Walter J. Scheirer, Vitomir Štuc; IEEE Transactions on Image Processing, vol. 29, pp. 2150-2165, 2020.

[pdf]


@ARTICLE{8866753,

author={K. {Grm} and W. J. {Scheirer} and V. {Štruc}}, j

ournal={IEEE Transactions on Image Processing},

title={Face Hallucination Using Cascaded Super-Resolution and Identity Priors},

year={2020},

volume={29},

number={},

pages={2150-2165},

}

Mateusz Trokielewicz,Adam Czajka, Piotr Maciejewicz, The 10th IEEE Int. Conference on Biometrics: Theory, Applications and Systems (BTAS 2019), Tampa, FL, USA

[arxiv]

Aidan Boyd,Adam Czajka, Kevin Bowyer, The 10th IEEE Int. Conference on Biometrics: Theory, Applications and Systems (BTAS 2019), Tampa, FL, USA

[arxiv]

Adam Czajka, Mateusz Trokielewicz, Piotr Maciejewicz; IEEE Spectrum, vol. 56, no. 09, pp. 44-49, Sept. 2019.

DOI: 10.1109/MSPEC.2019.8818591

@ARTICLE{8818591,

author={A. {Czajka} and M. {Trokielewicz} and P. {Maciejewicz}},

journal={IEEE Spectrum},

title={The Eyes have it: New iris-recognition techniques can tell whether an Eye is healthy, diseased—or dead},

year={2019},

volume={56},

number={09},

pages={44-49},

}

Kevin Bowyer, Michael King; Biometric Technology Today, vol.2019 (8), pp. 8-11.

DOI: 10.1016/S0969-4765(19)30114-6

P. Li, L. Prieto, D. Mery and P. J. Flynn, IEEE Transactions on Information Forensics and Security, vol. 14, no. 8, pp. 2000-2012, Aug. 2019.

DOI: 10.1109/TIFS.2018.2890812

[arxiv] [pdf]

@article{DBLP:journals/corr/abs-1805-11529,

  author    = {Pei Li and

               Loreto Prieto and

               Domingo Mery and

               Patrick J. Flynn},

  title     = {Low Resolution Face Recognition in the Wild},

  journal   = {CoRR},

  volume    = {abs/1805.11529},

  year      = {2018},

  url       = {http://arxiv.org/abs/1805.11529},

  archivePrefix = {arXiv},

  eprint    = {1805.11529},

  timestamp = {Wed, 06 Jun 2018 14:48:18 +0200},

  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1805-11529},

  bibsource = {dblp computer science bibliography, https://dblp.org}

}

Joseph Thompson, Patrick Flynn, Christopher Boehnen, Hector Santos-Villalobos; IEEE Transactions on Information Forensics and Security 14(8):2102-2112, August 2019.

DOI: 10.1109/TIFS.2018.2869342

@ARTICLE{8457272,

author={J. {Thompson} and P. {Flynn} and C. {Boehnen} and H. {Santos-Villalobos}},

journal={IEEE Transactions on Information Forensics and Security},

title={Assessing the Impact of Corneal Refraction and Iris Tissue Non-Planarity on Iris Recognition},

year={2019},

volume={14},

number={8},

pages={2102-2112},

}

Ishan Nigam, Rohit Keshari, Mayank Vatsa, Richa Singh, Kevin Bowyer; Sci Rep 9, 11139 (2019).

DOI: 10.1038/s41598-019-47222-4

[pdf]

Jeffery Kinnison, Mateusz Trokielewicz, Camila Carballo, Adam Czajka, Walter Scheirer, 2019 International Conference on Biometrics (ICB), Crete, Greece, 2019, pp. 1-8.

[arxiv]

@INPROCEEDINGS{8987377,

author={J. {Kinnison} and M. {Trokielewicz} and C. {Carballo} and A. {Czajka} and W. {Scheirer}},

booktitle={2019 International Conference on Biometrics (ICB)},

title={Learning-Free Iris Segmentation Revisited: A First Step Toward Fast Volumetric Operation Over Video Samples},

year={2019},

volume={},

number={},

pages={1-8},

}

Daniel Kerrigan, Mateusz Trokielewicz,Adam Czajka, Kevin Bowyer, 2019 International Conference on Biometrics (ICB), Crete, Greece, 2019, pp. 1-7.

[arxiv]

@INPROCEEDINGS{8987299,

author={D. {Kerrigan} and M. {Trokielewicz} and A. {Czajka} and K. W. {Bowyer}},

booktitle={2019 International Conference on Biometrics (ICB)},

title={Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks},

year={2019},

volume={},

number={},

pages={1-7},

}

Klemen Grm, Martin Pernus, Leo Cluzel,Walter J. Scheirer, Simon Dobrišek, Vitomir Štruc; IEEE Computer Society Workshop on Biometrics, June 2019.

[pdf]

@article{DBLP:journals/corr/abs-1812-09010,
author = {Klemen Grm and
Martin Pernus and
Leo Cluzel and
Walter J. Scheirer and
Simon Dobrisek and
Vitomir Struc},
title = {Face Hallucination Revisited: An Exploratory Study on Dataset Bias},
journal = {CoRR},
volume = {abs/1812.09010},
year = {2018},
url = {http://arxiv.org/abs/1812.09010},
archivePrefix = {arXiv},
eprint = {1812.09010},
timestamp = {Wed, 02 Jan 2019 14:40:18 +0100},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1812-09010},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

Kalman Tornai, Walter J. Scheirer; IAPR International Conference on Biometrics, June 2019.

[pdf]

@inproceedings{Tornai,
author = {Kalman Tornai and
Walter J. Scheirer},
title = {Gesture-based User Identity Verification as an Open Set Problem for Smartphones},
booktitle = {IAPR International Conference On Biometrics},
year = {2019},
}

Daniel P Benalcazar, Diego Bastias, Claudio A Perez, Kevin W Bowyer, EEE Access, vol. 7, pp. 61461-61472, 2019.

[pdf]

@ARTICLE{8710265,

author={D. P. {Benalcazar} and D. {Bastias} and C. A. {Perez} and K. W. {Bowyer}},

journal={IEEE Access},

title={A 3D Iris Scanner From Multiple 2D Visible Light Images},

year={2019},

volume={7},

number={},

pages={61461-61472},

}

Suraj Mishra,Adam Czajka, Peixian Liang, Danny Z. Chen, X. Sharon Hu, The IEEE Int. Symposium onBiomedical Imaging (ISBI), Venice, Italy, April 8-11, 2019.

[arxiv]

@INPROCEEDINGS{8759448,

author={S. {Mishra} and P. {Liang} and A. {Czajka} and D. Z. {Chen} and X. S. {Hu}},

booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},

title={CC-NET: Image Complexity Guided Network Compression for Biomedical Image Segmentation},

year={2019},

volume={},

number={},

pages={57-60},

}

Aaron D. Wellman, Sam C. Coad, Patrick J. Flynn, Ty K. Siam, Christopher P. McLellan; The Journal of Strength and Conditioning Research 33(4): 1020-1027, April 2019.

DOI: 10.1519/JSC.0000000000002173

Banerjee, Sreya and Scheirer, Walter J. Scheirer and Li, Lei,Frontiers in Computational Neuroscience, Feb 2019.

https://doi.org/10.3389/fncom.2019.00003

@ARTICLE{10.3389/fncom.2019.00003,
AUTHOR={Banerjee, Sreya and Scheirer, Walter J. and Li, Lei},   
TITLE={An Extreme Value Theory Model of Cross-Modal Sensory Information Integration in Modulation of Vertebrate Visual System Functions},      
JOURNAL={Frontiers in Computational Neuroscience},      
VOLUME={13},      
PAGES={3},     
YEAR={2019},
URL={https://www.frontiersin.org/article/10.3389/fncom.2019.00003},       
DOI={10.3389/fncom.2019.00003},      
ISSN={1662-5188},   
}


S. Banerjee, W. J. Scheirer, K. W. Bowyer and P. J. Flynn, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 2019, pp. 2126-2136.

DOI: 10.1109/WACV.2019.00230

[arxiv]

@INPROCEEDINGS{8658792,

author={S. {Banerjee} and W. J. {Scheirer} and K. W. {Bowyer} and P. J. {Flynn}},

booktitle={2019 IEEE Winter Conference on Applications of Computer Vision (WACV)},

title={Fast Face Image Synthesis With Minimal Training},

year={2019},

volume={},

number={},

pages={2126-2136},}

Daniel Benalcazar, Claudio Perez, Diego Bastias, Kevin Bowyer, 019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 2019, pp. 867-876.

@INPROCEEDINGS{8659059,

author={D. {Benalcazar} and C. {Perez} and D. {Bastias} and K. {Bowyer}},

booktitle={2019 IEEE Winter Conference on Applications of Computer Vision (WACV)},

title={Iris Recognition: Comparing Visible-Light Lateral and Frontal Illumination to NIR Frontal Illumination},

year={2019},

volume={},

number={},

pages={867-876},

}

Aparna Bharati, Daniel Moreira, Joel Brogan, Patricia Hale, Kevin Bowyer, Patrick Flynn, Anderson Rocha, Walter J. Scheirer, IEEE Winter Conference on Applications of Computer Vision (WACV), January 2019

DOI: 10.1109/WACV.2019.00185

[arxiv]

@INPROCEEDINGS{8658404,

author={A. {Bharati} and D. {Moreira} and J. {Brogan} and P. {Hale} and K. {Bowyer} and P. {Flynn} and A. {Rocha} and W. {Scheirer}},

booktitle={2019 IEEE Winter Conference on Applications of Computer Vision (WACV)},

title={Beyond Pixels: Image Provenance Analysis Leveraging Metadata},

year={2019},

volume={},

number={},

pages={1692-1702},

}

Adam Czajka, Zhaoyuan Fang, Kevin W. Bowyer, The IEEE Winter Conf. on Applications of Computer Vision, Waikoloa Village,Hawaii, January 7-11, 2019.

[arxiv]

@INPROCEEDINGS{8658879,

author={A. {Czajka} and Z. {Fang} and K. {Bowyer}},

booktitle={2019 IEEE Winter Conference on Applications of Computer Vision (WACV)},

title={Iris Presentation Attack Detection Based on Photometric Stereo Features},

year={2019}, volume={},

number={},

pages={877-885},

}

Daniel Moreira, Mateusz Trokielewicz,Adam Czajka, Kevin W. Bowyer, Patrick Flynn, Proc. Winter Conference on Applications of Computer Vision, 2019. 

DOI: 10.1109/WACV.2019.00105

[arxiv]

Adam Czajka, Daniel Moreira, Kevin W. Bowyer, Patrick Flynn, Proc. Winter Conference on Applications of Computer Vision 2019.

DOI: 10.1109/WACV.2019.00107

[arxiv]

Nathaniel Blanchard, Kyle Skinner, Aden Kemp, Walter Scheirer, Patrick Flynn; Proc. Winter Conference on Applications of Computer Vision, 2019.

DOI: 10.1109/WACV.2019.00150

Aaron D. Wellman, Sam C. Coad, Patrick J. Flynn, Ty K. Siam, Christopher P. McLellan; The Journal of Strength and Conditioning Research 33(1):112-124, January 2019.

DOI:10.1519:JSC.0000000000002169

Andrey Kuehlkamp, Kevin Bowyer; 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 2019, pp. 904-912.

[pdf]

@INPROCEEDINGS{8659186,

author={A. {Kuehlkamp} and K. {Bowyer}},

booktitle={2019 IEEE Winter Conference on Applications of Computer Vision (WACV)},

title={Predicting Gender From Iris Texture May Be Harder Than It Seems},

year={2019},

volume={},

number={},

pages={904-912},

}

Terrance Boult, Steve Cruz, Akshay Dhamija, Manuel Günther, James Henrydoss, Walter J. Scheirer; AAAI Conference on Artificial Intelligence (AAAI 2019), Senior Memebr  Track, January 2019.

[pdf]

@inproceedings{Boult18,
author = {Terrance Boult and
Akshay Dhamija and
Manuel Gunther and
James Henrydoss and
Walter J. Scheirer},
title = {Learning and the Unknown: Surveying Steps Toward Open World Recognition},
booktitle = {AAAI Conference on Artificial Intelligence (AAAI 2019)},
year = {2019},
}


2018

Bingyu Shen, Christopher W. Forstall, Anderson Rocha, Walter J. Scheirer; IEEE Access, vol. 6, pp. 41002-41012, 2018.

[pdf]

@ARTICLE{8412174,

author={B. {Shen} and C. W. {Forstall} and A. D. R. {Rocha} and W. J. {Scheirer}},

journal={IEEE Access},

title={Practical Text Phylogeny for Real-World Settings},

year={2018},

volume={6},

number={},

pages={41002-41012},

}

Adam CzajkaAndrey Kuehlkamp, Allan Pinto, Anderson Rocha, Kevin W. Bowyer,  EEE Transactions onInformation Security and Forensics, pp. 1-13, 2018, DOI: 10.1109/TIFS.2018.2878542.

[arxiv]

@ARTICLE{8513867, 

author={A. {Kuehlkamp} and A. {Pinto} and A. {Rocha} and K. W. {Bowyer} and A. {Czajka}}, 

journal={IEEE Transactions on Information Forensics and Security}, 

title={Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection},

year={2019}, 

volume={14}, 

number={6}, 

pages={1419-1431},

}

Mateusz Trokielewicz, Adam Czajka, The 9th IEEEInt. Conference on Biometrics: Theory, Applications and Systems (BTAS 2018), Los Angeles, CA, USA

[arxiv]

@InProceedings{Trokielewicz_BTAS_2018,

  author    = {Mateusz Trokielewicz and Adam Czajka and Piotr Maciejewicz},

  title     = {Recognition of Post-mortem Irises for Presentation Attack Detection},

  booktitle = {{IEEE} International Conference on Biometrics: Theory Applications and Systems (BTAS)},

  year      = {2018},

  pages     = {1-6},

  address   = {Los Angeles, CA, USA},

  month     = {October},

  publisher = {IEEE},

  abstract  = {This paper presents a deep-learning-based method for iris presentation attack detection (PAD) when iris images are obtained from deceased people. Post-mortem iris recognition, despite being a potentially useful method that could aid forensic identification, can also pose challenges when used inappropriately, i.e., by using a dead organ of a person in an unauthorized way. Our approach is based on the VGG-16 architecture fine-tuned with a database of 574 post-mortem, near-infrared iris images from the Warsaw-BioBase-PostMortem-Iris-v1 database, complemented by a dataset of 256 images of live irises, collected within the scope of this study. Experiments described in this paper show that our approach is able to correctly classify iris images as either representing a live or a dead eye in almost 99\% of the trials, averaged over 20 subject-disjoint, train/test splits. We also show that the post-mortem iris detection accuracy increases as time since death elapses, and that we are able to construct a classification system with APCER=0% @BPCER = 1% when only post-mortem samples collected at least 16 hours post-mortem are considered. Since acquisitions of ante- and post-mortem samples differ significantly, we applied countermeasures to minimize bias in our classification methodology caused by image properties that are not related to the PAD. This included using the same iris sensor in collection of ante- and post-mortem samples, and analysis of class activation mapping to ensure that the discriminant iris regions utilized by our classifier are related to properties of the eye, and not to the acquisition protocol. This paper offers the first known to us PAS method in a post-mortem setting, together with an explanation of the decisions made by the convolutional neural network. Along with the paper we offer source codes, weights of the trained network, and a dataset of live iris images to facilitate reproducibility and further research.},

  day       = {22-25},

}

Mel McCurie, Fernando Beletti, Lucas Parzianello, Allen Westendorp, Samuel E. Anthony, Walter J. Scheirer, Image and Vision Computing  (IVC), October 2018.

DOI: 10.1016/j.imavis.2018.06.010

[pdf]

Paper Page

@article{McCurie_2018_IVC,
author = {Mel McCurie and Fernando Beletti and Lucas Parzianello and Allen Westendorp and
Samuel E. Anthony and Walter J. Scheirer},
title = {Convolutional Neural Networks for Subjective Face Attributes},
journal = {Image and Vision Computing (IVC)},
volume = {78},
month = {October},
year = {2018}
}


Nathaniel Blanchard, Aparna Bharati, Daniel Moreira, Walter J. Scheirer, the First Workshop on Computational Modeling of Human Multimodal Language - ACL 2018, July 2018.
[pdf]

@inproceedings{blanchard_getting_2018,

    address = {Melbourne, Australia},

    title = {Getting the subtext without the text: {Scalable} multimodal sentiment classification from visual and acoustic modalities},

    copyright = {All rights reserved},

    booktitle = {Proceedings of the 56th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} ({ACL} 2018) ({First} {Workshop} and {Grand} {Challenge} on {Computational} {Modeling} of {Human} {Multimodal} {Language})},

    publisher = {Association for Computational Linguistics},

    author = {Blanchard, Nathaniel and Moreira, Daniel and Bharati, Aparna and Scheirer, Walter J.},

    year = {2018}

}

Adam Czajka, Benedict Becker, in: Sébastien Marcel, Mark Nixon, Julian Fierrez, Nicholas Evans (Eds.), "Handbook of Biometric Anti-Spoofing (2nd Edition)", 2018 (to appear).

[supp. material 1] [supp. material 2] [supp. material 3]

@InCollection{Czajka_PAD_Handbook_2018,

  author    = {Adam Czajka and Benedict Becker},

  title     = {{Application of Dynamic Features of the Pupil for Iris Presentation Attack Detection}},

  booktitle = {Handbook of Biometric Anti-Spoofing (2nd Edition, to appear)},

  publisher = {Springer International Publishing AG},

  year      = {2018},

  editor    = {S\'{e}bastien Marcel and Mark Nixon and Julian Fierrez and Nicholas Evans},

  pages     = {1--17},

  abstract  = {This chapter presents a comprehensive study on application of stimulated pupillary light reflex to presentation attack detection (PAD) that can be used in iris recognition systems. A pupil, when stimulated by visible light in a predefined manner, may offer sophisticated dynamic liveness features that cannot be acquired from dead eyes or other static objects such as printed contact lenses, paper printouts or prosthetic eyes. Modeling of pupil dynamics requires a few seconds of observation under varying light conditions that can be supplied by a visible light source in addition to the existing near-infrared illuminants used in iris image acquisition. The central element of the presented approach is an accurate modeling and classification of pupil dynamics that makes mimicking an actual eye reaction difficult. This chapter discusses new data-driven models of pupil dynamics based on recurrent neural networks and compares their PAD performance to solutions based on parametric Clynes-Kohn model and various classification techniques. Experiments with 166 distinct eyes of 84 subjects show that the best data-driven solution, one based on long-short term memory, was able to correctly recognize 99.97\% of attack presentations and 98.62\% of normal pupil reactions. In the approach using the Clynes-Kohn parametric model of pupil dynamics, we were able to perfectly recognize abnormalities and correctly recognize 99.97\% of normal pupil reactions on the same dataset with the same evaluation protocol as the data-driven approach. This means that the data-driven solutions favorably compare to the parametric approaches, which require model identification in exchange for a slightly better performance. We also show that observation times may be as short as 3 seconds when using the parametric model, and as short as 2 seconds when applying the recurrent neural network without substantial loss in accuracy. Along with this chapter we also offer: a) all time series representing pupil dynamics for 166 distinct eyes used in this study, b) weights of the trained recurrent neural network offering the best performance, c) source codes of the reference PAD implementation based on Clynes-Kohn parametric model, and d) all PAD scores that allow the reproduction of the plots presented in this chapter. To our best knowledge, this chapter proposes the first database of pupil measurements dedicated to presentation attack detection and the first evaluation of recurrent neural network-based modeling of pupil dynamics and PAD.},

}

Adam Czajka, Kevin Bowyer, ACM Computing Surveys, Vol. 51, No. 4, pp. 86:1–86:35, 2018, DOI: 10.1145/3232849

[arxiv]

@Article{Czajka_CSUR_2018,

  author    = {Adam Czajka and Kevin W. Bowyer},

  title     = {Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art},

  journal   = {{ACM} Computing Surveys},

  year      = {2018},

  volume    = {1},

  number    = {1},

  pages     = {1--35},

  issn      = {0360-0300},

  note      = {in press},

  acmid     = {3232849},

  address   = {New York, NY, USA},

  doi       = {10.1145/3232849},

  numpages  = {35},

  publisher = {ACM},

  url       = {http://doi.acm.org/10.1145/3232849},

}

Brandon RichardWebster, Samuel E. Anthony, Walter J. ScheirerIEEE Transactions on Pattern Analysis and Machine Intelligence, June 2018.

[pdf] [supp. material]

@ARTICLE{8395028,
author={B. RichardWebster and S. Anthony and W. Scheirer},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition},
year={2018},
volume={},
number={},
pages={1-1},
keywords={Computational modeling;Computer vision;Machine learning;Observers;Psychology;Task analysis;Visualization;Deep Learning;Evaluation;Neuroscience;Object Recognition;Psychology;Visual Psychophysics},
doi={10.1109/TPAMI.2018.2849989},
ISSN={0162-8828},
month={},
}

Mateusz Trokielewicz, Adam Czajka, 6th IAPR/IEEE International Workshop on Biometrics and Forensics (IWBF 2018), June 7 - 8, 2018, Sassari, Italy.

[pdf] [arxiv]

@InProceedings{Trokielewicz_IWBF_2018,

  author    = {Mateusz Trokielewicz and Adam Czajka},

  title     = {Data-Driven Segmentation of Post-mortem Iris Images},

  booktitle = {{IAPR/IEEE} International Conference on Biometrics and Forensics (IWBF)},

  year      = {2018},

  pages     = {1-6},

  address   = {Sassari, Italy},

  month     = {June},

  publisher = {IAPR/IEEE},

  abstract  = {This paper presents a method for segmenting iris images obtained from the deceased subjects, by training a deep convolutional neural network (DCNN) designed for the purpose of semantic segmentation. Post-mortem iris recognition has recently emerged as an alternative, or additional, method useful in forensic analysis. At the same time it poses many new challenges from the technological standpoint, one of them being the image segmentation stage, which has proven difficult to be reliably executed by conventional iris recognition methods. Our approach is based on the SegNet architecture, fine-tuned with 1,300 manually segmented post-mortem iris images taken from the Warsaw-BioBase-Post-Mortem-Iris v1.0 database. The experiments presented in this paper show that this data-driven solution is able to learn specific deformations present in post-mortem samples, which are missing from alive irises, and offers a considerable improvement over the state-of-the-art, conventional segmentation algorithm (OSIRIS): the Intersection over Union (IoU) metric was improved from 73.6% (for OSIRIS) to 83% (for DCNN-based presented in this paper) averaged over subject-disjoint, multiple splits of the data into train and test subsets. This paper offers the first known to us method of automatic processing of post-mortem iris images. We offer source codes with the trained DCNN that perform end-to-end segmentation of post-mortem iris images, as described in this paper. Also, we offer binary masks corresponding to manual segmentation of samples from Warsaw-BioBase-Post-Mortem-Iris v1.0 database to facilitate development of alternative methods for post-mortem iris segmentation.},

  day       = {7-8},

}

Mateusz Trokielewicz,Adam Czajka, Piotr Maciejewicz, IEEE Transactions  on  Information  Security  and  Forensics,  vol.  14,  no.  6,  pp.  1501-1514,  June  2019

[arxiv] [pdf]

@article{DBLP:journals/corr/abs-1804-01962,

  author    = {Mateusz Trokielewicz and

               Adam Czajka and

               Piotr Maciejewicz},

  title     = {Iris Recognition After Death},

  journal   = {CoRR},

  volume    = {abs/1804.01962},

  year      = {2018},

  url       = {http://arxiv.org/abs/1804.01962},

  archivePrefix = {arXiv},

  eprint    = {1804.01962},

  timestamp = {Tue, 01 May 2018 19:46:29 +0200},

  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1804-01962},

  bibsource = {dblp computer science bibliography, https://dblp.org}

}

Ewelina Bartuzi, Katarzyna Roszczewska, Andrzej Pacut, Adam Czajka, International Conference on Biometrics and Forensics, June 2018, Sassari, Italy.

@InProceedings{Bartuzi_IWBF_2018,

  author    = {Ewelina Bartuzi and Katarzyna Roszczewska and Andrzej Pacut and Adam Czajka},

  title     = {Unconstrained Biometric Recognition based on Thermal Hand Images},

  booktitle = {{IAPR/IEEE} International Conference on Biometrics and Forensics (IWBF)},

  year      = {2018},

  pages     = {1-6},

  address   = {Sassari, Italy},

  month     = {June},

  publisher = {IAPR/IEEE},

  abstract  = {This paper proposes a biometric recognition method based on thermal images of inner part of the hand, and a database of 21,000 thermal images of both hands acquired by a specialized thermal camera from 70 subjects. The data for each subject was acquired in three different sessions, with two first sessions organized on the same day, and the third session organized approximately two weeks apart. This allowed to analyze the stability of hand temperature in both short-term and long-term horizons. No hand stabilization or positioning devices were used during acquisition, making this setup closer to real-world, unconstrained applications. This required mak- ing our method translation-, rotation- and scale-invariant. Two approaches for feature selection and classification are proposed and compared: feature engineering deploying texture descriptors such as Binarized Statistical Image Features (BSIF) and Gabor wavelets, and feature learning based on convolutional neural networks (CNN) trained in different environmental conditions. For within-session scenario we achieved 0.36% and 0.00% of equal error rate (EER) in the first and the second approach, respectively. Between-session EER stands at 27.98% for the first approach and 17.17% for the second one. These results allow for estimation of a short-term stability of hand thermal information. This paper presents the first known to us database of hand thermal images and the first biometric system based solely on hand thermal maps acquired by thermal sensor in unconstrained scenario.},

  day       = {7-8},

}

Joel Brogan, Walter J. Scheirer, IEEE Intelligent Systems, May/June 2018.

[pdf]

@article{Brogan2018,
author = {Joel Brogan and
Walter J. Scheirer},
title = {Facial Frontalization and Smart Matching Via Pose},
journal = {IEEE Intelligent Systems},
volume = {33},
number = {3},
year = {2018}
}


D. Mery, S. Banerjee, International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

[pdf]

@article{meryrecognition,
  title={RECOGNITION OF FACES AND FACIAL ATTRIBUTES USING ACCUMULATIVE LOCAL SPARSE REPRESENTATIONS},
  author={Mery, Domingo and Banerjee, Sandipan}
}

@article{Sunderhauf2018,
author = {Niko Sunderhauf and
Oliver Brock and
Walter J. Scheirer and
Raia Hadsell and
Dieter Fox and
Jurgen Leitner and
Ben Upcroft and
Pieter Abbeel and
Wolfram Burgard and
Michael Milford and
Peter Corke},
title = {The Limits and Potentials of Deep Learning for Robotics},
journal = {International Journal of Robotics Research},
volume = {37},
number = {4-5},
year = {2018}
}

Brandon RichardWebster, So Yon Kwon, Christopher Clarizio, Samuel E. Anthony, Walter J. Scheirer,  European Conference on Computer Vision 2018 15 (2018): n. pag. Web. doi:10.1007/978-3-030-01267-0.

[pdf] [supp. material]

@article{DBLP:journals/corr/abs-1803-07140,
  author    = {Brandon RichardWebster and
               So Yon Kwon and
               Christopher Clarizio and
               Samuel E. Anthony and
               Walter J. Scheirer},
  title     = {Visual Psychophysics for Making Face Recognition Algorithms More Explainable},
  journal   = {CoRR},
  volume    = {abs/1803.07140},
  year      = {2018},
  url       = {http://arxiv.org/abs/1803.07140},
  archivePrefix = {arXiv},
  eprint    = {1803.07140},
  timestamp = {Wed, 11 Apr 2018 11:12:46 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1803-07140},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Andrey Kuehlkamp, Kevin Bowyer, ScienceDirect, March 2018.

[pdf]

@article{KUEHLKAMP201817,

title = "Found a good match: Should I keep searching? — Accuracy and performance in iris matching using 1-to-First search",

journal = "Image and Vision Computing",

volume = "73",

pages = "17 - 27",

year = "2018",

issn = "0262-8856",

doi = "https://doi.org/10.1016/j.imavis.2018.03.003",

url = "http://www.sciencedirect.com/science/article/pii/S026288561830026X",

author = "Andrey Kuehlkamp and Kevin Bowyer",

keywords = "Biometrics, Iris recognition, Error rates, Identification, Accuracy, Search, 1:First, 1:N, Open-set"

}

Sandipan Banerjee*Joel Brogan*, Aparna Bharati, Brandon RichardWebster, Vitomir StrucPatrick FlynnWalter J. ScheirerProceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), March 2018.
* denotes equal contribution
[pdf] [arxiv][oral] [poster] [code]

@InProceedings{Banerjee_2017_IJCB,
author = {Sandipan Banerjee and Joel Brogan and Aparna Bharati and
Brandon RichardWebster and Vitomir Struc and Patrick Flynn
and Walter J. Scheirer},
title = {To Frontalize or Not to Frontalize: Do We Really Need Elaborate Pre-processing to Improve Face Recognition?},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2018}
}

Rosaura Vidal Mata*Sreya Banerjee*Klemen GrmVitomir StrucWalter J. ScheirerProceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), March 2018.
* denotes equal contribution

@InProceedings{vidal2018ug,
title={UG$^{2}$: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition},
author={Vidal, Rosaura G and Banerjee, Sreya and Grm, Klemen and Struc, Vitomir and Scheirer, Walter J},
booktitle={IEEE Winter Conference on Applications of Computer Vision (WACV)},
year={2018}
}

Jeff KinnisonNathaniel Kremer-HermanDouglas ThainWalter J. ScheirerProceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), March 2018.

@InProceedings{KinnisonKTS17,
author = {Jeff Kinnison and
Nathaniel Kremer{-}Herman and
Douglas Thain and
Walter J. Scheirer},
title = {{SHADHO:} Massively Scalable Hardware-Aware Distributed Hyperparameter
Optimization},
booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
year = {2018}
}

Ronald MetoyerQiyu ZhiBart JanczukWalter J. ScheirerProceedings of the ACM International Conference on Intelligent User Interfaces (IUI), March 2018.

@inProceedings{MetoyerA18,
author = {Ronald Metoyer and
Qiyu Zhi and
Bart Janczuk and
Walter J. Scheirer},
title = {Coupling Story to Visualization: Using Textual Analysis as a Bridge Between Data and Interpretation},
booktitle = {ACM International Conference on Intelligent User Interfaces (IUI)},
year = {2018}
}

Ethan RuddLalit P. JainWalter J. ScheirerTerrance BoultIEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), March 2018.

@article{Rudd_2018_TPAMI,
author = {Ethan Rudd and Lalit P. Jain and Walter J. Scheirer and Terrance Boult},
title = {The Extreme Value Machine},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)},
volume = {40},
number = {3},
month = {March},
year = {2018}
}

Ruth C. FongWalter J. ScheirerDavid D. CoxScientific Reports, March 2018.

@article{fong2018using,
title={Using human brain activity to guide machine learning},
author={Fong, Ruth C and Scheirer, Walter J and Cox, David D},
journal={Scientific reports},
volume={8},
number={1},
pages={5397},
year={2018},
publisher={Nature Publishing Group}
}

Allan Pinto, Helio Pedrini, Michael Krumdick, Benedict Becker, Adam Czajka, Kevin W. Bowyer, Anderson Rocha, Vatsa et al.(Eds.), "Deep Learning in Biometrics", CRC Press, March 2018.

[pdf]

@InCollection{Pinto_DLB_2018,

  pages     = {245--293},

  address   = {Boca Raton, London, New York},

  title     = {Counteracting Presentation Attacks in Face, Fingerprint, and Iris Recognition},

  publisher = {CRC Press},

  year      = {2018},

  author    = {Allan Pinto and Helio Pedrini and Michael Krumdick and Benedict Becker and Adam Czajka and Kevin W. Bowyer and Anderson Rocha},

  editor    = {Mayank Vatsa, Richa Singh, Angshul Majumdar},

  isbn      = {9781138578234},

  booktitle = {Deep Learning in Biometrics},

  url       = {https://www.crcpress.com/Deep-Learning-in-Biometrics/Vatsa-Singh-Majumdar/p/book/9781138578234},

}

Pei Li, Maria Loreto Prieto, Patrick J. Flynn, Domingo MeryFebruary 2018.

[pdf]

@INPROCEEDINGS{8272704,
author={P. Li and M. L. Prieto and P. J. Flynn and D. Mery},
booktitle={2017 IEEE International Joint Conference on Biometrics (IJCB)},
title={Learning face similarity for re-identification from real surveillance video: A deep metric solution},
year={2017},
volume={},
number={},
pages={243-252},
keywords={cameras;face recognition;feature extraction;image matching;image representation;neural nets;video surveillance;body features;deep metric solution;face misalignment;face similarity;facial ReID approaches;fully convolutional structure;high-quality dataset;municipal rapid transit system;patch matching technique;person re-identification featuring faces;spatial pyramid pooling;surveillance cameras;surveillance network;surveillance systems;surveillance video;Agriculture;Cameras;Detectors;Face;Face detection;Protocols;Surveillance}, 
doi={10.1109/BTAS.2017.8272704},
ISSN={},
month={Oct},
}

Daniel Moreira, Aparna Bharati, Joel Brogan, Allan Pinto, Michael Parowski, Kevin W Bowyer, Patrick J Flynn, Anderson Rocha, Walter J Scheirer, EEE Transactions on Image Processing, vol. 27, no. 12, pp. 6109-6123, Dec. 2018.

DOI: 10.1109/TIP.2018.2865674

[arxiv] [pdf] [video]

@article{DBLP:journals/corr/abs-1801-06510,

  author    = {Daniel Moreira and

               Aparna Bharati and

               Joel Brogan and

               Allan da Silva Pinto and

               Michael Parowski and

               Kevin W. Bowyer and

               Patrick J. Flynn and

               Anderson Rocha and

               Walter J. Scheirer},

  title     = {Image Provenance Analysis at Scale},

  journal   = {CoRR},

  volume    = {abs/1801.06510},

  year      = {2018},

  url       = {http://arxiv.org/abs/1801.06510},

  archivePrefix = {arXiv},

  eprint    = {1801.06510},

  timestamp = {Fri, 02 Feb 2018 14:20:25 +0100},

  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1801-06510},

  bibsource = {dblp computer science bibliography, https://dblp.org}

}

2017

Sandipan BanerjeeJohn S. BernhardWalter J. ScheirerKevin Bowyer, Patrick FlynnProceedings of the IAPR/IEEE International Joint Conference on Biometrics (IJCB), October 2017.

@InProceedings{Banerjee_2017_IJCB,
author = {Sandipan Banerjee and John S. Bernhard and Walter J. Scheirer and Kevin Bowyer and Patrick Flynn},
title = {SREFI: Synthesis of Realistic Example Face Images},
booktitle = {The IAPR/IEEE International Joint Conference on Biometrics (IJCB)},
month = {October},
year = {2017}
}

David Yambay, Benedict Becker, Naman Kohli, Daksha Yadav, Adam Czajka, Kevin W. Bowyer, Stephanie Schuckers, Richa Singh, Mayank Vatsa, Afzel Noore, Diego Gragnaniello, Carlo Sansone, Luisa Verdoliva, Lingxiao He, Yiwei Ru, Haiqing Li, Nianfeng Liu, Zhenan Sun, Tieniu Tan, International Joint Conference on Biometrics - IJCB 2017, October 1-4, 2017, Denver, Colorado, USA.

[pdf]

@InProceedings{Yambay_IJCB_2017,

  author    = {David Yambay and Benedict Becker and Naman Kohli and Daksha Yadav and Adam Czajka and Kevin W. Bowyer and Stephanie Schuckers and Richa Singh and Mayank Vatsa and Afzel Noore and Diego Gragnaniello and C. Sansone and L. Verdoliva and Lingxiao He and Yiwei Ru and Haiqing Li and Nianfeng Liu and Zhenan Sun and Tieniu Tan},

  publisher = {IEEE},

  address   = {Denver, CO, USA},

  title     = {{LivDet Iris 2017} -- Iris Liveness Detection Competition 2017},

  booktitle = {{IEEE} International Joint Conference on Biometrics (IJCB)},

  year      = {2017},

  pages     = {1-6},

  abstract  = {Presentation attacks such as using a contact lens with a printed pattern or printouts of an iris can be utilized to bypass a biometric security system. Solutions which have been proposed to counteract this vulnerability are called Presentation Attack Detection (also commonly called live- ness detection or anti-spoofing) and detect the presence of such attacks. The first international iris liveness competi- tion was launched in 2013 in order to assess the perfor- mance of currently available PAD algorithms with a second competition in 2015. This paper presents detailed results of its third edition, LivDet-Iris 2017. Three software-based approaches to Presentation Attack Detection were submit- ted. Four datasets of live and spoof images were tested with an additional cross-sensor test. New datasets and novel sit- uations of data have resulted in this competition being of a higher difficulty than previous competitions. Anonymous received the best results with a rate of rejected live samples of 3.36% and rate of accepted spoof samples of 14.71%. The results show that even with advances, print-out iris attacks as well as patterned contacts lenses are still difficult for software-based systems to detect. Printed iris images were easier to be differentiated from live images in compar- ison to patterned contact lenses as was also seen in previous competitions.},

}

Aparna Bharati, Mayank Vatsa, Richa Singh, Kevin W. Bowyer, and Xin Tong. IEEE International Joint Conference on Biometrics (IJCB), pp. 474-482. 2017.

[pdf]

@INPROCEEDINGS{8272732, 

author={A. Bharati and M. Vatsa and R. Singh and K. W. Bowyer and X. Tong}, 

booktitle={2017 IEEE International Joint Conference on Biometrics (IJCB)}, 

title={Demography-based facial retouching detection using subclass supervised sparse autoencoder}, 

year={2017}, 

volume={}, 

number={}, 

pages={474-482}, 

keywords={demography;face recognition;feature extraction;image classification;image coding;image denoising;image representation;learning (artificial intelligence);MultiDemographic Retouched Faces dataset;demography;digital retouching;face image;facial retouching detection;generalized retouching detection;retouched images;retouching software packages;semisupervised autoencoder;sparse autoencoder;testing images;training;Databases;Face;Feature extraction;Shape;Skin;Support vector machines;Tools}, 

doi={10.1109/BTAS.2017.8272732}, 

ISSN={}, 

month={Oct},}

Renhao Liu, Lawrence Hall, Kevin Bowyer, Dmitry Goldgof, Robert Gatenby, Kaoutar Ben Ahmed, IEEE International Conference on Systems, Man, and Cybernetics, October 2017, Banf.

[pdf]

@INPROCEEDINGS{8122802, 

author={R. Liu and L. O. Hall and K. W. Bowyer and D. B. Goldgof and R. Gatenby and K. Ben Ahmed}, 

booktitle={2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, 

title={Synthetic minority image over-sampling technique: How to improve AUC for glioblastoma patient survival prediction}, 

year={2017}, 

volume={}, 

number={}, 

pages={1357-1362}, 

keywords={Gaussian noise;diseases;feature extraction;image classification;image denoising;learning (artificial intelligence);medical image processing;sampling methods;tumours;AUC;Glioblastoma patient survival group prediction;Synthetic minority image over-sampling;feature vector level;glioblastoma patient survival prediction;histogram feature extraction;image level;medical data;medical images;minority class predictive accuracy;over-sampling minority class;synthetic feature vectors;synthetic minority class;uniform local binary patterns;Cancer;Feature extraction;Gaussian noise;Histograms;Predictive models;Tumors}, 

doi={10.1109/SMC.2017.8122802}, 

ISSN={}, 

month={Oct},}

Diego Bastias; Claudio A. Perez; Daniel P. Benalcazar; Kevin W. Bowyer Claudio Perez, Diego Bastias and Kevin Bowyer, IEEE International Joint Conference on Biometrics, October 2017, Denver, Colorado.

[pdf]

@INPROCEEDINGS{8272735, 

author={D. Bastias and C. A. Perez and D. P. Benalcazar and K. W. Bowyer}, 

booktitle={2017 IEEE International Joint Conference on Biometrics (IJCB)}, 

title={A method for 3D iris reconstruction from multiple 2D near-infrared images}, 

year={2017}, 

volume={}, 

number={}, 

pages={503-509}, 

keywords={biometrics (access control);eye;feature extraction;image matching;image recognition;image reconstruction;image segmentation;image texture;infrared imaging;iris recognition;3D iris reconstruction;biometric identification;depth information;infrared iris images;iris enhancement;iris image acquisition;iris recognition;iris surface;iris texture equalization;occlusions;reliable current technique;reliable identification;Cameras;Iris;Iris recognition;Lighting;Solid modeling;Three-dimensional displays;Two dimensional displays}, 

doi={10.1109/BTAS.2017.8272735}, 

ISSN={}, 

month={Oct},}

Joel Brogan, Paolo Bestagini, Aparna Bharati, Allan Pinto, Daniel Moreira, Kevin BowyerPatrick Flynn, Anderson Rocha, Walter J. ScheirerProceedings of the IEEE International Conference on Image Processing (ICIP), September 2017.
[pdf] [code]

@InProceedings{Brogan_2017_ICIP,
author = {Joel Brogan and
Paolo Bestagini and
Aparna Bharati and
Allan da Silva Pinto and
Daniel Moreira and
Kevin W. Bowyer and
Patrick J. Flynn and
Anderson Rocha and
Walter J. Scheirer},
title = {Spotting the Difference: Context Retrieval and Analysis for Improved
Forgery Detection and Localization},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
year = {2017}
}

Aparna Bharati, Daniel Moreira, Allan Pinto, Joel Brogan, Kevin Bowyer, Patrick FlynnWalter J. ScheirerAnderson RochaProceedings of the IEEE International Conference on Image Processing (ICIP), September 2017.

@InProceedings{Bharati_2017_ICIP,
author = {Aparna Bharati and
Daniel Moreira and
Allan da Silva Pinto and
Joel Brogan and
Kevin W. Bowyer and
Patrick J. Flynn and
Walter J. Scheirer and
Anderson Rocha},
title = {U-Phylogeny: Undirected Provenance Graph Consruction in the Wild},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
year = {2017}
}

Allan Pinto, Daniel Moreira, Aparna Bharati, Joel Brogan, Kevin Bowyer, Patrick FlynnWalter J. ScheirerAnderson RochaProceedings of the IEEE International Conference on Image Processing (ICIP), September 2017.

@InProceedings{Pinto_2017_ICIP,
author = {Allan da Silva Pinto and
Daniel Moreira and
Aparna Bharati and
Joel Brogan and
Kevin W. Bowyer and
Patrick J. Flynn and
Walter J. Scheirer and
Anderson Rocha},
title = {Provenance Filtering for Multimedia Phylogeny},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
year = {2017}
}

Jianxu ChenSreya Banerjee, Abhinav GramaWalter J. ScheirerDanny Z. ChenProceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2017.

@InProceedings{Chen_2017_MICCAI,
author = {Jianxu Chen and Sreya Banerjee and Abhinav Grama and Walter J. Scheirer and Danny Z. Chen},
title = {Neuron Segmentation Using Deep Complete Bipartite Networks},
booktitle = {International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)},
month = {September},
year = {2017}
}

Adam JacobsonWalter J. Scheirer, Michael MilfordProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2017.

@InProceedings{Jacobson_2017_IROS,
author = {Adam Jacobson and Walter J. Scheirer and Michael Milford},
title = {Deja vu: Scalable Place Recognition Using Mutually Supportive Feature Frequencies},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
month = {September},
year = {2017}
}

Adam Czajka, Kevin Bowyer, Michael Krumdick, Rosaura Vidal Mata, IEEE Transactions on Information Forensics and Security, Vol. 12, No. 9, pp. 2184-2196, Sept. 2017.

[pdf]

@Article{Czajka_TIFS_2017,

  author    = {Adam Czajka and Kevin W. Bowyer and Michael Krumdick and Rosaura G. VidalMata},

  title     = {Recognition of Image-Orientation-Based Iris Spoofing},

  journal   = {{IEEE} Transactions on Information Forensics and Security},

  year      = {2017},

  volume    = {12},

  number    = {9},

  pages     = {2184-2196},

  month     = {September},

  issn      = {1556-6013},

  abstract  = {This paper presents a solution to automatically recognize the correct left/right and upright/upside-down orientation of iris images. This solution can be used to counter spoofing attacks directed to generate fake identities by rotating an iris image or the iris sensor during the acquisition. Two approaches are compared on the same data, using the same evaluation protocol: 1) feature engineering, using hand-crafted features classified by a support vector machine (SVM) and 2) feature learning, using data-driven features learned and classified by a convolutional neural network (CNN). A data set of 20 750 iris images, acquired for 103 subjects using four sensors, was used for development. An additional subject-disjoint data set of 1,939 images, from 32 additional subjects, was used for testing purposes. Both same-sensor and cross-sensor tests were carried out to investigate how the classification approaches generalize to unknown hardware. The SVM-based approach achieved an average correct classification rate above 95% (89%) for recognition of left/right (upright/upside-down) orientation when tested on subject-disjoint data and camera-disjoint data, and 99% (97%) if the images were acquired by the same sensor. The CNN-based approach performed better for same-sensor experiments, and presented slightly worse generalization capabilities to unknown sensors when compared with the SVM. We are not aware of any other papers on the automatic recognition of upright/upside-down orientation of iris images, or studying both hand-crafted and data-driven features in same-sensor and cross-sensor subject-disjoint experiments. The data sets used in this paper, along with random splits of the data used in cross-validation, are being made available.},

  doi       = {10.1109/TIFS.2017.2701332},

  keywords  = {cameras;feature extraction;image classification;image sensors;iris recognition;learning (artificial intelligence);support vector machines;CNN-based approach;SVM-based approach;camera-disjoint data;classification approaches;convolutional neural network;cross-sensor subject-disjoint experiments;cross-sensor tests;data-driven feature learning;feature engineering;image-orientation-based iris spoofing recognition;iris image left/right orientation;iris image upright/upside-down orientation;iris sensor;same-sensor subject-disjoint experiments;same-sensor tests;spoofing attacks;subject-disjoint data;support vector machine;Ducts;Head;Image recognition;Iris;Iris recognition;Neural networks;Support vector machines;Iris recognition;presentation attack detection;spoofing},

  publisher = {IEEE},

}

Adam Czajka, Kevin W. Bowyer, Estefan Ortiz,IET Biometrics, Vol. 7, No. 2, pp. 136-144, 2017

DOI:10.1049/iet-bmt.2016.0191

[paper]

@Article{Czajka_IET_2017,

  author    = {Adam Czajka and Kevin W. Bowyer and Estefan Ortiz},

  title     = {Analysis of diurnal changes in pupil dilation and eyelid aperture},

  journal   = {IET Biometrics},

  year      = {2018},

  volume    = {7},

  pages     = {136-144(8)},

  month     = {March},

  issn      = {2047-4938},

  abstract  = {This work is inspired by the observation of surprising daily fluctuations in the number of valid iris code bits used to match irises in the NEXUS program operated by the Canadian Border Security Agency. These fluctuations have an impact on iris comparison scores but cannot be simply explained by pupil dilation, which does not have a clear pattern that would generalise to a population. To check if fluctuations in the number of valid iris code bits may be explained by eyelid aperture observed in a controlled, laboratory environment, the eyelid aperture was measured for 18 subjects participating in an acquisition every 2 h during the day. Simultaneously, the pupil dilation was measured to check the existence of a daily pattern for a population and for single subjects. There are two interesting outcomes of this work. First, there are statistically significant changes during the day in both pupil dilation and eyelid opening observed for individual subjects. Second, these changes do not generalise well into a common pattern for the group. Consequently, the diurnal fluctuations in the number of bits compared and the comparison score observed in the NEXUS program cannot be explained by changes in pupil dilation nor by eyelid aperture.},

  copyright = {© The Institution of Engineering and Technology},

  issue     = {2},

  keywords  = {pupil dilation;diurnal fluctuations;iris matching;eyelid aperture;NEXUS program;valid iris code bit daily fluctuations;Canadian Border Security Agency;iris comparison scores;diurnal change analysis;},

  language  = {English},

  publisher = {Institution of Engineering and Technology},

  url       = {http://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2016.0191},

}

Tomas Larrain, John Bernhard, Domingo Mery and Kevin W. Bowyer, IEEE Transactions on Information Forensics and Security 12 (7), 1646-1657, July 2017.

[pdf]

@ARTICLE{7875165, 

author={T. Larrain and J. S. Bernhard and D. Mery and K. W. Bowyer}, 

journal={IEEE Transactions on Information Forensics and Security}, 

title={Face Recognition Using Sparse Fingerprint Classification Algorithm}, 

year={2017}, 

volume={12}, 

number={7}, 

pages={1646-1657}, 

keywords={face recognition;fingerprint identification;image classification;image representation;query processing;SFCA;dictionary;face recognition;query image;sparse fingerprint classification algorithm;sparse representation;Dictionaries;Face;Face recognition;Feature extraction;Fingerprint recognition;Lighting;Training;Face recognition}, 

doi={10.1109/TIFS.2017.2680403}, 

ISSN={1556-6013}, 

month={July},}

Andrey Kuehlkamp, Benedict Becker, Kevin Bowyer, IEEE, May 2017.

[pdf]

@INPROCEEDINGS{7926716,

author={A. Kuehlkamp and B. Becker and K. Bowyer},

booktitle={2017 IEEE Winter Conference on Applications of Computer Vision (WACV)},

title={Gender-from-Iris or Gender-from-Mascara?},

year={2017},

volume={},

number={},

pages={1151-1159},

keywords={feedforward neural nets;image classification;image segmentation;image texture;multilayer perceptrons;classifiers;convolutional neural networks;cosmetics effect;data-driven features;eyelash occlusion;gender-from-iris problem;gender-from-iris texture;hand-crafted features;multilayer perceptron;person-disjoint train;segmentation;Feature extraction;Image segmentation;Iris;Iris recognition;Support vector machines;Testing;Training},

doi={10.1109/WACV.2017.133},

ISSN={},

month={March},

}

Jason M Grant, Patrick J Flynn, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), ACM, vol. 13, pp. 1-23, May 2017.

DOI: 10.1145/3052930

[link]

@article{Grant:2017:CSU:3058792.3052930,

 author = {Grant, Jason M. and Flynn, Patrick J.},

 title = {Crowd Scene Understanding from Video: A Survey},

 journal = {ACM Trans. Multimedia Comput. Commun. Appl.},

 issue_date = {May 2017},

 volume = {13},

 number = {2},

 month = mar,

 year = {2017},

 issn = {1551-6857},

 pages = {19:1--19:23},

 articleno = {19},

 numpages = {23},

 url = {http://doi.acm.org/10.1145/3052930},

 doi = {10.1145/3052930},

 acmid = {3052930},

 publisher = {ACM},

 address = {New York, NY, USA},

 keywords = {Crowd analysis, datasets, human activity},

Mel McCurieFernando Beletti, Lucas ParzianelloAllen WestendorpSamuel E. AnthonyWalter J. ScheirerProceedings of the IEEE Conference on Automatic Face and Gesture Recognition (FG), May 2017.

@InProceedings{McCurie_2017_FG,
author = {Mel McCurie and Fernando Beletti and Lucas Parzianello and Allen Westendorp and
Samuel E. Anthony and Walter J. Scheirer},
title = {Predicting First Impressions with Deep Learning},
booktitle = {IEEE Conference on Automatic Face and Gesture Recognition (FG)},
month = {May},
year = {2017}
}

S. Banerjee, J. Sweet, C. Sweet, M. Lieberman, Winter Conference on the Applications of Computer Vision (WACV), April 2017.

[pdf] [arxiv] [oral] [poster] [ND info page]

@article{DBLP:journals/corr/BanerjeeSSL17,
author = {Sandipan Banerjee and
James Sweet and
Christopher Sweet and
Marya Lieberman},
title = {Visual Recognition of Paper Analytical Device Images for Detection
of Falsified Pharmaceuticals},
journal = {CoRR},
volume = {abs/1704.04251},
year = {2017},
url = {http://arxiv.org/abs/1704.04251},
archivePrefix = {arXiv},
eprint = {1704.04251},
timestamp = {Wed, 07 Jun 2017 14:43:14 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/BanerjeeSSL17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

D. Mery, E. Svec, M. Arias, V. Riffo, J. Saavedra, S. Banerjee IEEE Trans. Systems, Man, and Cybernetics: Systems (SMC) 47 (4), pp. 682 - 692, April 2017.

[pdf]

@ARTICLE{7775025, 

author={D. Mery and E. Svec and M. Arias and V. Riffo and J. M. Saavedra and S. Banerjee}, 

journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems}, 

title={Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection}, 

year={2017}, 

volume={47}, 

number={4}, 

pages={682-692}, 

keywords={X-ray imaging;authorisation;automatic optical inspection;bags;computer vision;object recognition;GDXray database;X-ray screening systems;X-ray testing;access control;automated recognition;bag of words;baggage inspection;computer vision;deep features;deep learning;human inspection task;object recognition;pattern recognition;security checkpoints;sparse representations;visual vocabularies;Computer vision;Image recognition;Inspection;Object recognition;Testing;Weapons;X-ray imaging;Baggage screening;X-ray testing;deep learning;implicit shape model (ISM);object categorization;object detection;object recognition;sparse representations;threat objects}, 

doi={10.1109/TSMC.2016.2628381}, 

ISSN={2168-2216}, 

month={April},

}

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz, 10th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 2017), February 21 - 23, 2017, Porto, Portugal.

[pdf]

@Conference{Trokielewicz_BIOSIGNALS_2017,

  author       = {Mateusz Trokielewicz and Adam Czajka and Piotr Maciejewicz},

  title        = {{Iris Recognition under Biologically Troublesome Conditions -- Effects of Aging, Diseases and Post-mortem Changes}},

  booktitle    = {Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies -- Volume 4: BIOSIGNALS, (BIOSTEC 2017)},

  year         = {2017},

  pages        = {253-258},

  organization = {INSTICC},

  publisher    = {SciTePress},

  abstract     = {This paper presents the most comprehensive analysis of iris recognition reliability in the occurrence of various biological processes happening naturally and pathologically in the human body, including aging, illnesses, and post-mortem changes to date. Insightful conclusions are offered in relation to all three of these aspects. Extensive regression analysis of the template aging phenomenon shows that differences in pupil dilation, combined with certain quality factors of the sample image and the progression of time itself can significantly degrade recognition accuracy. Impactful effects can also be observed when iris recognition is employed with eyes affected by certain eye pathologies or (even more) with eyes of the deceased subjects. Notably, appropriate databases are delivered to the biometric community to stimulate further research in these utterly important areas of iris biometrics studies. Finally, some open questions are stated to inspire further discussions and research on th ese important topics. To Authors’ best knowledge, this is the only scientific study of iris recognition reliability of such a broad scope and novelty.},

  doi          = {10.5220/0006251702530258},

  isbn         = {978-989-758-212-7},

}

Walter J. ScheirerMorgan & Claypool Publishers, February 2017.

@book{Scheirer_2017_MC,
author = {Walter J. Scheirer},
title = {Extreme Value Theory-Based Methods for Visual Recognition},
publisher = {Morgan & Claypool Publishers},
month = {February},
year = {2017}
}

P. Jonathon Phillips, Patrick J. Flynn, Kevin W. Bowyer, Image and Vision Computing Journal 58, 96–107, February 2017.

[pdf]

@article{PHILLIPS201796,

title = "Lessons from collecting a million biometric samples",

journal = "Image and Vision Computing",

volume = "58",

pages = "96 - 107",

year = "2017",

issn = "0262-8856",

doi = "https://doi.org/10.1016/j.imavis.2016.08.004",

url = "http://www.sciencedirect.com/science/article/pii/S0262885616301287",

author = "P. Jonathon Phillips and Patrick J. Flynn and Kevin W. Bowyer",

keywords = "Face recognition, Algorithm performance, Human performance, Challenge problem"

}

Anderson RochaWalter J. ScheirerChristopher W. ForstallThiago CavalcanteAntonio TheophiloBingyu ShenAriadne R. B. CarvalhoEfstathios StamatatosIEEE Transactions on Information Forensics and Security (T-IFS), January 2017.
[pdf]

@article{Scheirer_2017_TIFS,
author = {Anderson Rocha and Walter J. Scheirer and Thiago Cavalcante and Antonio Theophilo
and Bingyu Shen and Ariadne R. B. Carvalho and Efstathios Stamatatos},
title = {Authorship Attribution for Social Media Forensics},
journal = {IEEE Transactions on Information Forensics and Security (T-IFS)},
volume = {12},
issue = {1},
month = {January},
year = {2017}
}

Massimo Tistarelli, Ross Beveridge, Patrick Flynn, Michele Nappi, Image and Vision Computing, pp. 108-109, 2017.

[link]

@article{Tistarelli2017,

affiliation = {University of Sassari, Italy; Colorado State University, USA; University of Notre Dame, USA; University of Salerno, Italy},

author = {Tistarelli, Massimo and Beveridge, Ross and Flynn, Patrick and Nappi, Michele},

doi = {10.1016/j.imavis.2017.01.008},

journal = {Image and Vision Computing},

language = {English},

number = {Complete},

pages = {108-109},

title = {Guest Editors’ Introduction},

volume = {58},

year = {2017},

}

2016

Pei Li, Joel Brogan, Patrick J. FlynnIEEE, December 2016.
[pdf]

@INPROCEEDINGS{7791204, 

author={P. Li and J. Brogan and P. J. Flynn}, 

booktitle={2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS)}, 

title={Toward facial re-identification: Experiments with data from an operational surveillance camera plant}, 

year={2016}, 

volume={}, 

number={}, 

pages={1-8}, 

keywords={face recognition;feature extraction;image capture;image matching;image resolution;video surveillance;body ReID method;facial reidentification;facial resolution;low resolution facial features;municipal rapid transit system;operational surveillance camera plant;person reidentification;Bridges;Cameras;Feature extraction;Image color analysis;Image resolution;Neural networks;Principal component analysis}, 

doi={10.1109/BTAS.2016.7791204}, 

ISSN={}, 

month={Sept},

}

Kevin W. Bowyer and Mark J. Burge, editors, Springer, 2016.

[link] [afterword]

@book{bowyer2016handbook,

  title={Handbook of iris recognition},

  author={Bowyer, Kevin W and Burge, Mark J},

  year={2016},

  publisher={Springer}

}

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz, 8th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS 2016), September 6 - 9, 2016, Buffalo, NY, USA.

[pdf]

@InProceedings{Trokielewicz_BTAS_2016,

  author    = {Mateusz Trokielewicz and Adam Czajka and Piotr Maciejewicz},

  title     = {Human iris recognition in post-mortem subjects: Study and database},

  booktitle = {{IEEE} International Conference on Biometrics: Theory Applications and Systems (BTAS)},

  publisher = {IEEE},

  address   = {Niagara Falls, NY, USA},

  year      = {2016},

  pages     = {1-6},

  month     = {Sept},

  abstract  = {This paper presents a unique study of post-mortem human iris recognition and the first known to us database of near-infrared and visible-light iris images of deceased humans collected up to almost 17 days after death. We used four different iris recognition methods to analyze the dynamics of iris quality decay in short-term comparisons (samples collected up to 60 hours after death) and long-term comparisons (for samples acquired up to 407 hours after demise). This study shows that post-mortem iris recognition is possible and occasionally works even 17 days after death. These conclusions contradict a promulgated rumor that iris is unusable shortly after decease. We make this dataset publicly available to let others verify our findings and to research new aspects of this important and unfamiliar topic. We are not aware of any earlier papers offering post-mortem human iris images and such comprehensive analysis employing four different matchers.},

  doi       = {10.1109/BTAS.2016.7791175},

  keywords  = {eye;iris recognition;deceased humans;iris quality decay;near-infrared iris images;post-mortem human iris recognition;time 17 day;time 407 h;time 60 h;visible-light iris images;Biomedical imaging;Cameras;Cornea;Databases;Iris recognition},

}

Walter J. ScheirerPatrick J. FlynnChangxing DingGuodong GuoVitomir StrucMohamad Al Jazaery, Klemen Grm, Simon Dobrisek, Dacheng Tao, Yu Zhu, Joel Brogan, Sandipan Banerjee, Aparna Bharati, Brandon RichardWebsterProceedings of the IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), September 2016.
[pdf]

@InProceedings{Scheirer_2016_BTAS,
author = {Walter J. Scheirer and Patrick J. Flynn and Changxing Ding and Guodong Guo and Vitomir Struc and Mohamad Al Jazaery and Klemen Grm and Simon Dobrisek and Dacheng Tao and Yu Zhu and Joel Brogan and Sandipan Banerjee and Aparna Bharati and Brandon RichardWebster},
title = {Report on the BTAS 2016 Video Person Recognition Evaluation},
booktitle = {Proceedings of the IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)},
year = {2016}
}

Domingo Mery, Yuning Zhao and Kevin Bowyer, IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), September 2016.

[pdf]

@INPROCEEDINGS{7791188, 

author={D. Mery and Y. Zhao and K. Bowyer}, 

booktitle={2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS)}, 

title={On accuracy estimation and comparison of results in biometric research}, 

year={2016}, 

volume={}, 

number={}, 

pages={1-8}, 

keywords={emotion recognition;face recognition;expression recognition;face recognition;gender classification;Databases;Estimation;Face;Face recognition;Protocols;Testing;Training}, 

doi={10.1109/BTAS.2016.7791188}, 

ISSN={}, 

month={Sept},}

Ishan Manjani, Hakki Sumerkan, Patrick J. Flynn and Kevin W. Bowyer, IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), September 2016.

[pdf]

@inproceedings{manjani2016template,

  title={Template aging in 3D and 2D face recognition},

  author={Manjani, Ishan and Sumerkan, Hakki and Flynn, Patrick J and Bowyer, Kevin W},

  booktitle={Biometrics Theory, Applications and Systems (BTAS), 2016 IEEE 8th International Conference on},

  pages={1--6},

  year={2016},

  organization={IEEE}

}

Kevin W. Bowyer and Patrick J. Flynn, IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), September 2016.

[pdf]

@INPROCEEDINGS{7791176, 

author={K. W. Bowyer and P. J. Flynn}, 

booktitle={2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS)}, 

title={Biometric identification of identical twins: A survey}, 

year={2016}, 

volume={}, 

number={}, 

pages={1-8}, 

keywords={biometrics (access control);security of data;biometric identification;biometric uncertainty;identical twins;DNA;Databases;Face;Face recognition;Fingerprint recognition;Fingers}, 

doi={10.1109/BTAS.2016.7791176}, 

ISSN={}, 

month={Sept},}

Estefan Ortiz and Kevin W. Bowyer, IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), September 2016.

[pdf]

@INPROCEEDINGS{7791194, 

author={E. Ortiz and K. W. Bowyer}, 

booktitle={2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS)}, 

title={Pitfalls in studying #x201C;big data #x201D; from operational scenarios}, 

year={2016}, 

volume={}, 

number={}, 

pages={1-7}, 

keywords={Big Data;iris recognition;Big Data;NISTIREX VI report;operational biometric dataset;operational iris recognition dataset;operational scenarios;Aging;Airports;Big data;High definition video;Iris;Iris recognition;Probes}, 

doi={10.1109/BTAS.2016.7791194}, 

ISSN={}, 

month={Sept},}

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz, Image and Vision Computing, Elsevier, 2016.

[pdf]

@Article{Trokielewicz_IMAVIS_2017,

  author   = {Mateusz Trokielewicz and Adam Czajka and Piotr Maciejewicz},

  title    = {Implications of ocular pathologies for iris recognition reliability},

  journal  = {Image and Vision Computing},

  year     = {2017},

  volume   = {58},

  pages    = {158 - 167},

  issn     = {0262-8856},

  abstract = {This paper presents an analysis of how iris recognition is influenced by eye disease and an appropriate dataset comprising 2996 images of irises taken from 230 distinct eyes (including 184 affected by more than 20 different eye conditions). The images were collected in near infrared and visible light during routine ophthalmological examination. The experimental study carried out utilizing four independent iris recognition algorithms (MIRLIN, VeriEye, OSIRIS and IriCore) renders four valuable results. First, the enrollment process is highly sensitive to those eye conditions that obstruct the iris or cause geometrical distortions. Second, even those conditions that do not produce visible changes to the structure of the iris may increase the dissimilarity between samples of the same eyes. Third, eye conditions affecting the geometry or the tissue structure of the iris or otherwise producing obstructions significantly decrease same-eye similarity and have a lower, yet still statistically significant, influence on impostor comparison scores. Fourth, for unhealthy eyes, the most prominent effect of disease on iris recognition is to cause segmentation errors. To our knowledge this paper describes the largest database of iris images for disease-affected eyes made publicly available to researchers and offers the most comprehensive study of what we can expect when iris recognition is employed for diseased eyes.},

  doi      = {https://doi.org/10.1016/j.imavis.2016.08.001},

  keywords = {Iris recognition, Ocular disease, Biometrics, Ophthalmology},

  url      = {http://www.sciencedirect.com/science/article/pii/S0262885616301251},

}

[pdf]

@article{Joesch_2016_eLife,
author = {Joesch, Maximilian and Mankus, David and Yamagata, Masahito and Shahbazi, Ali and Schalek, Richard and
Suissa-Peleg, Adi and Meister, Markus and Lichtman, Jeff W. and Scheirer, Walter J. and Sanes, Joshua R.},
title = {Reconstruction of Genetically Identified Neurons Imaged by Serial-Section Electron Microscopy},
journal = {eLife},
volume = {5},
pages = {e15015},
year = {2016}
}

Jianxu Chen, Feng Shen, Danny Z Chen, Patrick J Flynn, IEEE Transactions on Information Forensics and Security, vol. 11, no. 7, pp. 1476-1485, July 2016.

@article{chen2013iris,

  title={Iris Recognition Based on Human-Interpretable},

  author={Chen, Jianxu and Shen, Feng and Chen, Danny Z and Flynn, Patrick J},

  year={2013}

}

Jianxu Chen, Feng Shen, Danny Ziyi Chen, Patrick J Flynn, IEEE Transactions on Information Forensics and Security, IEEE, vol. 11, pp. 1476-1485, July 2016.

[pdf]

@ARTICLE{7422104, 

author={J. Chen and F. Shen and D. Z. Chen and P. J. Flynn}, 

journal={IEEE Transactions on Information Forensics and Security}, 

title={Iris Recognition Based on Human-Interpretable Features}, 

year={2016}, 

volume={11}, 

number={7}, 

pages={1476-1485}, 

keywords={iris recognition;human recognition;human-friendly techniques;human-in-the-loop iris recognition system;human-interpretable features;iris recognition;matching scheme;visible-feature-based iris recognition method;Algorithm design and analysis;Cryptography;Feature extraction;Forensics;Image segmentation;Iris recognition;Probes;Iris recognition;forensics;human-in-the-loop;iris recognition;visible feature}, 

doi={10.1109/TIFS.2016.2535901}, 

ISSN={1556-6013}, 

month={July},}

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz, 9th IAPR International Conference on Biometrics (ICB 2016), June 13 - 16, 2016, Halmstad, Sweden.

[pdf]

@InProceedings{Trokielewicz_ICB_2016,

  author    = {Mateusz Trokielewicz and Adam Czajka and Piotr Maciejewicz},

  title     = {Post-mortem human iris recognition},

  booktitle = {{IEEE} International Conference on Biometrics (ICB)},

  address   = {Halmstad, Sweden},

  publisher = {IEEE},

  year      = {2016},

  pages     = {1-6},

  month     = {June},

  abstract  = {This paper presents a unique analysis of post-mortem human iris recognition. Post-mortem human iris images were collected at the university mortuary in three sessions separated by approximately 11 hours, with the first session organized from 5 to 7 hours after demise. Analysis performed for four independent iris recognition methods shows that the common claim of the iris being useless for biometric identification soon after death is not entirely true. Since the pupil has a constant and neutral dilation after death (the so called “cadaveric position”), this makes the iris pattern perfectly visible from the standpoint of dilation. We found that more than 90% of irises are still correctly recognized when captured a few hours after death, and that serious iris deterioration begins approximately 22 hours later, since the recognition rate drops to a range of 13.3-73.3% (depending on the method used) when the cornea starts to be cloudy. There were only two failures to enroll (out of 104 images) observed for only a single method (out of four employed in this study). These findings show that the dynamics of post-mortem changes to the iris that are important for biometric identification are much more moderate than previously believed. To the best of our knowledge, this paper presents the first experimental study of how iris recognition works after death, and we hope that these preliminary findings will stimulate further research in this area.},

  doi       = {10.1109/ICB.2016.7550073},

  keywords  = {feature extraction;iris recognition;biometric identification;iris pattern;postmortem human iris recognition;Biomedical imaging;Cameras;Cornea;Degradation;Iris recognition;Pathology;Visualization},

}

Adam Czajka, in: Kevin W. Bowyer, Mark J. Burge (Eds.), Handbook of Iris Recognition, Second Edition, pp. 439-467, Springer-Verlag London, 2016.

[pdf]

@incollection{Czajka_Handbook_2016,

  pages     = {439--467},

  title     = {Iris Liveness Detection by Modeling Dynamic Pupil Features},

  publisher = {Springer London},

  year      = {2016},

  author    = {Czajka, Adam},

  editor    = {Bowyer, Kevin W. and Burge, Mark J.},

  address   = {London},

  isbn      = {978-1-4471-6784-6},

  abstract  = {The objective of this chapter is to present how to employ pupil dynamics in eye liveness detection. A thorough review of current liveness detection methods is provided at the beginning of the chapter to make the scientific background and position this method within current state-of-the-art methodology. Pupil dynamics may serve as a component of a wider presentation attack detection in iris recognition systems, making them more secure. Due to a lack of public databases that would support this research, we have built our own iris capture device to register pupil size changes under visible light stimuli, and registered 204 observations for 26 subjects (52 different irides), each containing 750 iris images taken every 40 ms. Each measurement registers the spontaneous pupil oscillations and its reaction after a sudden increase and a sudden decrease of the intensity of visible light. The Kohn and Clynes pupil dynamics model is used to describe these changes; hence, we convert each observation into a point in a feature space defined by model parameters. To answer the question whether the eye is alive (that is, if it reacts to light changes as a human eye) or the presentation is suspicious (that is, if it reacts oddly or no reaction is observed), we use linear and nonlinear support vector machines to classify natural reaction and spontaneous oscillations, simultaneously investigating the goodness of fit to reject bad modeling. Our experiments show that this approach can achieve a perfect performance for the data we have collected; all normal reactions are correctly differentiated from spontaneous oscillations. We investigated three variants of modeling to find the simplest, yet still powerful configuration of the method, namely (1) observing the pupil reaction to both the positive and negative changes in the light intensity, (2) using only the pupil reaction to positive surge of the light intensity, and (3) employing only the pupil reaction when the light is suddenly turned off. Further investigation related to the shortest observation time required to model the pupil reaction led to the final conclusion that time periods not exceeding 3 s are adequate to offer a perfect performance (on this dataset).},

  booktitle = {Handbook of Iris Recognition},

  comment   = {iris:PAD:dynamic:active},

  doi       = {10.1007/978-1-4471-6784-6_19},

  url       = {http://dx.doi.org/10.1007/978-1-4471-6784-6_19},

}

Adam Czajka, Włodzimierz Kasprzak, Artur Wilkowski, Bulletin of the Polish Academy of Sciences. Technical Sciences, Vol. 64, No. 4, pp. 807-819, 2016.

[pdf]

@Article{Czajka_BPASTS_2016,

  author    = {Adam Czajka and W\lodzimierz Kasprzak and Artur Wilkowski},

  title     = {Verification of iris image authenticity using fragile watermarking},

  journal   = {Bulletin of the Polish Academy of Sciences Technical Sciences},

  year      = {2016},

  volume    = {64},

  pages     = {807-819},

  month     = {December},

  issn      = {2300-1917},

  abstract  = {This paper proposes and evaluates a watermarking-based approach to certify the authenticity of iris images when they are captured by a genuine equipment. In the proposed method, the iris images are secretly signed before being used in biometric processes, and the resulting signature is embedded into the JPEG carrier image in the DCT domain in a data-dependent way. Any alteration of the original (certified) image makes the signature no longer corresponding to this image and this change can be quickly identified at the receiver site. Hence, it is called fragile watermarking to differentiate this method from regular watermarking that should present some robustness against image alterations. There is no need to attach any auxiliary signature data, hence the existing, already standardized transmission channels and storage protocols may be used. The embedding procedure requires to remove some part of the original information. But, by using the BATH dataset comprising 32 000 iris images collected for 1 600 distinct eyes, we verify that the proposed alterations have no impact on iris recognition reliability, although statistically significant, small differences in genuine score distributions are observed when the watermark is embedded to both the enrollment and verification iris images. This is a unique evaluation of how the watermark embedding of digital signatures into the ISO CROPPED iris images (during the enrollment, verification or both) influences the reliability of a well-established, commercial iris recognition methodology. Without loss in generality, this approach is targeted to biometric-enabled ID documents that deploy iris data to authenticate the holder of the document.},

  day       = {23},

  doi       = {https://doi.org/10.1515/bpasts-2016-0090},

  issue     = {4},

  keywords  = {biometric data authentication; iris recognition; steganography; watermarking},

  language  = {English},

  publisher = {De Gruyter},

  url       = {https://doi.org/10.1515/bpasts-2016-0090},

}

Estefan Ortiz, Kevin W Bowyer, Patrick J Flynn, IET Biometrics, IET Digital Library, vol. 5, pp. 92-99, June 2016.

[link]

@ARTICLE{
   iet:/content/journals/10.1049/iet-bmt.2015.0005,
   author = {Estefan Ortiz},
   affiliation = {               <xhtml:span xml:lang="en">Computer Science and Engineering, University of Notre Dame, 384 Fitzpatrick Hall, Notre Dame, IN 46556, USA</xhtml:span>            },
   author = {Kevin W. Bowyer},
   affiliation = {               <xhtml:span xml:lang="en">Computer Science and Engineering, University of Notre Dame, 384 Fitzpatrick Hall, Notre Dame, IN 46556, USA</xhtml:span>            },
   author = {Patrick J. Flynn},
   affiliation = {               <xhtml:span xml:lang="en">Computer Science and Engineering, University of Notre Dame, 384 Fitzpatrick Hall, Notre Dame, IN 46556, USA</xhtml:span>            },
   keywords = {pupil dilation;probe image;matching accuracy improvement;enrolment image;dilation ratios;false nonmatch result probability;best-eye image;median dilation;iris recognition accuracy improvement;optimal single-eye image dilation-aware enrolment;},
   ISSN = {2047-4938},
   language = {English},
   abstract = {Iris recognition systems typically enrol a person based on a single ‘best’ eye image. Research has shown that the probability of a false non-match result increases with increased difference in pupil dilation between the enrolment image and the probe image. Therefore, dilation-aware methods of enrolment should improve the accuracy of iris recognition. The authors examine a strategy to improve accuracy through a dilation-aware enrolment step that selects one or more enrolment images based on the observed distribution of dilation ratios for that eye. Additionally, they demonstrate that an image with median dilation is the optimal single eye image dilation-aware enrolment choice. Their results confirm that this dilation-aware enrolment strategy does improve matching accuracy compared with traditional single-image enrolment, and also compared with multi-image enrolment that does not take dilation into account.},
   title = {Dilation-aware enrolment for iris recognition},
   journal = {IET Biometrics},
   issue = {2},   
   volume = {5},
   year = {2016},
   month = {June},
   pages = {92-99(7)},
   publisher ={Institution of Engineering and Technology},
   copyright = {© The Institution of Engineering and Technology},
   url = {http://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2015.0005}
}

Andrey Kuehlkamp, Kevin W. Bowyer, IEEE, May 2016.

[pdf]

@INPROCEEDINGS{7477687,

author={A. Kuehlkamp and K. W. Bowyer},

booktitle={2016 IEEE Winter Conference on Applications of Computer Vision (WACV)},

title={An analysis of 1-to-first matching in iris recognition},

year={2016},

volume={},

number={},

pages={1-8},

keywords={image matching;iris recognition;search problems;1-to-N search;1-to-first matching analysis;below-threshold match;false match error rate;identification mode;iris images;iris recognition systems;modified 1-to-first search;Computer science;Databases;Error analysis;Iris recognition;Probes;Robustness},

doi={10.1109/WACV.2016.7477687},

ISSN={},

month={March},

}

Aparna Bharati, Richa Singh, Mayank Vatsa, Kevin W. Bowyer, IEEE Transactions on Information Forensics and Security (TIFS) 11, no. 9, 1903-1913, 2016.

[pdf] [dataset]

@ARTICLE{7464282, 
author={A. Bharati and R. Singh and M. Vatsa and K. W. Bowyer}, 
journal={IEEE Transactions on Information Forensics and Security}, 
title={Detecting Facial Retouching Using Supervised Deep Learning}, 
year={2016}, 
volume={11}, 
number={9}, 
pages={1903-1913}, 
keywords={Boltzmann machines;Web sites;face recognition;image matching;learning (artificial intelligence);visual databases;Boltzmann machine algorithm;automatic face recognition;detecting facial retouching;digital alterations;face images;identification cards;image databases;image matching;photo sharing Web sites;social media;supervised deep learning;unaltered gallery image;Classification algorithms;Databases;Face;Face recognition;Media;Probes;Skin;Image forensics;biometric spoofing;face image alteration;face image retouching;face recognition}, 
doi={10.1109/TIFS.2016.2561898}, 
ISSN={1556-6013}, 
month={Sept},

}

Juan Tapia, Claudio Perez and Kevin W. Bowyer, IEEE Transactions on Information Forensics and Security, 2016.

[pdf]

@ARTICLE{7447785, 

author={J. E. Tapia and C. A. Perez and K. W. Bowyer}, 

journal={IEEE Transactions on Information Forensics and Security}, 

title={Gender Classification From the Same Iris Code Used for Recognition}, 

year={2016}, 

volume={11}, 

number={8}, 

pages={1760-1770}, 

keywords={feature selection;image classification;image texture;iris recognition;binary iris code;feature selection;gender classification;gender prediction;iris code;iris recognition;iris region;iris texture features;Feature extraction;Image segmentation;Iris;Iris recognition;Mutual information;Testing;Training;Feature Selection;Gender Classification;Gender classification;Iris;feature selection;iris}, 

doi={10.1109/TIFS.2016.2550418}, 

ISSN={1556-6013}, 

month={Aug},}

Walter J. ScheirerChristopher W. ForstallNeil CoffeeLiterary and Linguistic Computing (LLC), April 2016.
[pdf]

@article{Scheirer_2016_LLC,
author = {Walter J. Scheirer and Christopher W. Forstall and Neil Coffee},
title = {The Sense of a Connection: Automatic Tracing of Intertextuality by Meaning},
journal = {Literary and Linguistic Computing (LLC)},
volume = {31},
issue = {1},
month = {April},
year = {2016}
}

David Yambay, Brian Walczak, Stephanie Schuckers, Adam Czajka, IEEE, February 2017.

[pdf]

@InProceedings{Yambay_ISBA_2017,

  author    = {David Yambay and Brian Walczak and Stephanie Schuckers and Adam Czajka},

  title     = {LivDet-Iris 2015 - Iris Liveness Detection Competition 2015},

  booktitle = {{IEEE} International Conference on Identity, Security and Behavior Analysis (ISBA)},

  year      = {2017},

  publisher = {IEEE},

  address   = {New Delhi, India},

  pages     = {1-6},

  month     = {Feb},

  abstract  = {Presentation attacks such as printed iris images or patterned contact lenses can be used to circumvent an iris recognition system. Different solutions have been proposed to counteract this vulnerability with Presentation Attack Detection (commonly called liveness detection) being used to detect the presence of an attack, yet independent evaluations and comparisons are rare. To fill this gap we have launched the first international iris liveness competition in 2013. This paper presents detailed results of its second edition, organized in 2015 (LivDet-Iris 2015). Four software-based approaches to Presentation Attack Detection were submitted. Results were tallied across three different iris datasets using a standardized testing protocol and large quantities of live and spoof iris images. The Federico Algorithm received the best results with a rate of rejected live samples of 1.68% and rate of accepted spoof samples of 5.48%. This shows that simple static attacks based on paper printouts and printed contact lenses are still challenging to be recognized purely by software-based approaches. Similar to the 2013 edition, printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses.},

  doi       = {10.1109/ISBA.2017.7947701},

  keywords  = {Error analysis;Image color analysis;Iris;Iris recognition;Lenses;Training},

}

Nisha Srinivas, Patrick J Flynn, Richard W Vorder Bruegge, Journal of forensic sciences, vol. 61, pp. S117-S130, January 2016.

[pdf]

@article{srinivas2016human,

  title={Human Identification Using Automatic and Semi-Automatically Detected Facial Marks},

  author={Srinivas, Nisha and Flynn, Patrick J and Vorder Bruegge, Richard W},

  journal={Journal of forensic sciences},

  volume={61},

  pages={S117--S130},

  year={2016},

  publisher={Wiley Online Library}

}

2015

Domingo Mery and Kevin W. Bowyer, Pattern Recognition Letters 68, 260-269, December 2015.

[pdf]

@article{MERY2015260,

title = "Automatic facial attribute analysis via adaptive sparse representation of random patches",

journal = "Pattern Recognition Letters",

volume = "68",

pages = "260 - 269",

year = "2015",

note = "Special Issue on “Soft Biometrics”",

issn = "0167-8655",

doi = "https://doi.org/10.1016/j.patrec.2015.05.005",

url = "http://www.sciencedirect.com/science/article/pii/S0167865515001506",

author = "Domingo Mery and Kevin Bowyer",

keywords = "Soft biometrics, Expression recognition, Gender recognition, Race recognition, Sparse representations, Facial attribute analysis"

}

Kevin W. Bowyer and Estefan Ortiz, IET Biometrics 4 (4), 192-199, 2015.

[pdf]

@article{bowyer2015critical,

  title={Critical examination of the IREX VI results},

  author={Bowyer, Kevin W and Ortiz, Estefan},

  journal={IET Biometrics},

  volume={4},

  number={4},

  pages={192--199},

  year={2015},

  publisher={IET}

}

Ajita RattaniWalter J. ScheirerArun RossIEEE Transactions on Information Forensics and Security (T-IFS), November 2015.

@article{Rattani_2015_TIFS,
author = {Ajita Rattani and Walter J. Scheirer and Arun Ross},
title = {Open Set Fingerprint Spoof Detection Across Novel Fabrication Materials},
journal = {IEEE Transactions on Information Forensics and Security (T-IFS)},
volume = {10},
issue = {11},
month = {November},
year = {2015}
}

Leonardo A. Cament, Francisco J. Galdames, Kevin W. Bowyer, and Claudio Perez, Pattern Recognition 48 (11),  3371-3384, November 2015.

[pdf]

@article{CAMENT20153371,

title = "Face recognition under pose variation with local Gabor features enhanced by Active Shape and Statistical Models",

journal = "Pattern Recognition",

volume = "48",

number = "11",

pages = "3371 - 3384",

year = "2015",

issn = "0031-3203",

doi = "https://doi.org/10.1016/j.patcog.2015.05.017",

url = "http://www.sciencedirect.com/science/article/pii/S003132031500196X",

author = "Leonardo A. Cament and Francisco J. Galdames and Kevin W. Bowyer and Claudio A. Perez",

keywords = "Face recognition across pose, Statistical model for face recognition, Active shape model, Gabor features, Entropy weighting"

}

James S. Doyle and Kevin W. Bowyer, IEEE Access, volume 3, 1672-1683, 14 September 2015.

@ARTICLE{7264974, 

author={J. S. Doyle and K. W. Bowyer}, 

journal={IEEE Access}, 

title={Robust Detection of Textured Contact Lenses in Iris Recognition Using BSIF}, 

year={2015}, 

volume={3}, 

number={}, 

pages={1672-1683}, 

keywords={image classification;image segmentation;image texture;iris recognition;object detection;BSIF;classification rate;iris recognition;iris region segmentation;iris sensor;robust textured contact lenses detection;Classification algorithms;Contact lenses;Detection algorithms;Detectors;Eyes;Image processing;Image segmentation;Iris recognition;Lenses;Biometrics;image classification;image processing;image texture analysis;machine learning}, 

doi={10.1109/ACCESS.2015.2477470}, 

ISSN={}, 

month={},}

Nikhil Yadav, Christian Poellabauer, Louis Daudet, Tomás Collins, Shane McQuillan, Patrick Flynn, Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, ACM, pp. 77-85, September 2015.

[pdf]

@inproceedings{Yadav:2015:PND:2808719.2808727,

 author = {Yadav, Nikhil and Poellabauer, Christian and Daudet, Louis and Collins, Tom\'{a}s and McQuillan, Shane and Flynn, Patrick},

 title = {Portable Neurological Disease Assessment Using Temporal Analysis of Speech},

 booktitle = {Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics},

 series = {BCB '15},

 year = {2015},

 isbn = {978-1-4503-3853-0},

 location = {Atlanta, Georgia},

 pages = {77--85},

 numpages = {9},

 url = {http://doi.acm.org/10.1145/2808719.2808727},

 doi = {10.1145/2808719.2808727},

 acmid = {2808727},

 publisher = {ACM},

 address = {New York, NY, USA},

 keywords = {concussions, portable diagnostics, speech analysis, voice pathology},

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz, IEEE Seventh International Conference on Biometrics: Theory, Applications and Systems (BTAS 2015), September 8-11, 2015, Arlington, USA.

[pdf]

@InProceedings{Trokielewicz_BTAS_2015,

  author    = {Mateusz Trokielewicz and Adam Czajka and Piotr Maciejewicz},

  title     = {Assessment of iris recognition reliability for eyes affected by ocular pathologies},

  booktitle = {{IEEE} International Conference on Biometrics: Theory Applications and Systems (BTAS)},

  year      = {2015},

  pages     = {1-6},

  address   = {Arlington, USA},

  month     = {September},

  abstract  = {This paper presents an analysis of how the iris recognition is impacted by eye diseases and an appropriate dataset comprising 2996 iris images of 230 distinct eyes (including 184 illness-affected eyes representing more than 20 different eye conditions). The images were collected in near infrared and visible light during a routine ophthalmological practice. The experimental study shows four valuable results. First, the enrollment process is highly sensitive to those eye conditions that make the iris obstructed or introduce geometrical distortions. Second, even those conditions that do not produce visible changes to the iris structure may increase the dissimilarity among samples of the same eyes. Third, eye conditions affecting iris geometry, its tissue structure or producing obstructions significantly decrease the iris recognition reliability. Fourth, for eyes afflicted by a disease, the most prominent effect of the disease on iris recognition is to cause segmentation errors. To our knowledge this is the first database of iris images for disease-affected eyes made publicly available to researchers, and the most comprehensive study of what we can expect when the iris recognition is deployed for non-healthy eyes.},

  day       = {8--11},

  doi       = {10.1109/BTAS.2015.7358747},

  keywords  = {diseases;eye;image segmentation;infrared imaging;iris recognition;medical image processing;vision defects;disease-affected eyes;eye conditions;eye diseases;geometrical distortions;illness-affected eyes;iris geometry;iris image database;iris recognition reliability assessment;iris structure;near infrared;ocular pathologies;ophthalmological practice;segmentation errors;tissue structure;visible changes;visible light;Cornea;Databases;Diseases;Iris;Iris recognition;Lenses;Pathology},

}

Adam Czajka, Kevin W. Bowyer, accepted for presentation at the IEEE Seventh International Conference on Biometrics: Theory, Applications and Systems (BTAS 2015), September 8-11, 2015, Arlington, USA.

[pdf]

@InProceedings{,

  author    = {A. Czajka and K. W. Bowyer},

  title     = {Statistical evaluation of up-to-three-attempt iris recognition},

  booktitle = {{IEEE} International Conference on Biometrics Theory, Applications and Systems (BTAS)},

  year      = {2015},

  pages     = {1-6},

  month     = {Sept},

  abstract  = {Real-world biometric applications often operate in the context of an identity transaction that allows up to three attempts. That is, if a biometric sample is acquired and if it does not result in a match, the user is allowed to acquire a second sample, and if it again does not result in a match, the user is allowed to acquire a third sample. If the third sample does not result in a match, then the transaction is ended with no match. We report results of an experiment to determine whether or not successive attempts can be considered as independent samples from the same distribution, and whether and how the quality of a biometric sample changes in successive attempts. To our knowledge, this is the first published research to investigate the statistics of multi-attempt biometric transactions. We find that the common assumption that the attempt outcomes come from independent and identically distributed random variables in multi-attempt biometric transactions is incorrect.},

  doi       = {10.1109/BTAS.2015.7358797},

  keywords  = {image matching;iris recognition;statistical analysis;biometric application;biometric matching;biometric sample acquisition;biometric sample quality;distributed random variables;identity transaction;multiattempt biometric transaction;statistical evaluation;up-to-three-attempt iris recognition;Authentication;Cameras;High definition video;Iris recognition;Sociology;Statistics},

}

John Bernhard, Jeremiah Barr, Kevin W. Bowyer and Patrick J. Flynn, IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS 2015).

[pdf]

@INPROCEEDINGS{7358780, 

author={J. Bernhard and J. Barr and K. W. Bowyer and P. Flynn}, 

booktitle={2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS)}, 

title={Near-IR to visible light face matching: Effectiveness of pre-processing options for commercial matchers}, 

year={2015}, 

volume={}, 

number={}, 

pages={1-8}, 

keywords={face recognition;feature extraction;image matching;infrared imaging;ND-NIVL;commercial matchers;face recognition;illumination issues;image preprocessing techniques;near infrared visible light database;near-IR face images;red plane extraction;standard visible light face matching;visible light images;Adaptive equalizers;Databases;Face;Histograms;Image resolution;Lighting;Springs}, 

doi={10.1109/BTAS.2015.7358780}, 

ISSN={}, 

month={Sept},}

Anderson RochaWalter J. ScheirerEURASIP Journal on Image and Video Processing (JIVP), July 2015.
[pdf]

@article{Rocha_2015_JIVP,
author = {Anderson Rocha and Walter J. Scheirer},
title = {Large-scale learning for media understanding},
journal = {EURASIP Journal on Image and Video Processing (JIVP)},
volume = {2015},
number = {24},
month = {July},
year = {2015}
}

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz, The 2nd IEEE International Conference on Cybernetics CYBCONF 2015, Special Session on Reliable Biometrics BIORELIABILITY 2015, Gdynia, June 24 - 26, Gdynia, Poland.

[pdf]

@InProceedings{Trokielewicz_CYBCONF_2015,

  author    = {M. Trokielewicz and A. Czajka and P. Maciejewicz},

  title     = {Database of iris images acquired in the presence of ocular pathologies and assessment of iris recognition reliability for disease-affected eyes},

  booktitle = {{IEEE} International Conference on Cybernetics (CYBCONF)},

  year      = {2015},

  pages     = {495-500},

  address   = {Gdynia, Poland},

  month     = {June},

  publisher = {IEEE},

  abstract  = {This paper presents a database of iris images collected from disease affected eyes and an analysis related to the influence of ocular diseases on iris recognition reliability. For that purpose we have collected a database of iris images acquired for 91 different eyes during routine ophthalmology visits. This collection gathers samples for healthy eyes as well as those with various eye pathologies, including cataract, acute glaucoma, posterior and anterior synechiae, retinal detachment, rubeosis iridis, corneal vascularization, corneal grafting, iris damage and atrophy and corneal ulcers, haze or opacities. To our best knowledge this is the first database of such kind that will be made publicly available. In the analysis the data were divided into five groups of samples presenting similar anticipated impact on iris recognition: 1) healthy (no impact), 2) unaffected, clear iris (although the illness was detected), 3) geometrically distorted irides, 4) distorted iris tissue and 5) obstructed iris tissue. Three different iris recognition methods (MIRLIN, VeriEye and OSIRIS) were then used to find differences in average genuine and impostor comparison scores calculated for healthy eyes and those impacted by a disease. Specifically, we obtained significantly worse genuine comparison scores for all iris matchers and all disease-affected eyes when compared to a group of healthy eyes, what have a high potential of impacting false non-match rate.},

  day       = {24--26},

  doi       = {10.1109/CYBConf.2015.7175984},

  keywords  = {diseases;eye;image matching;iris recognition;visual databases;MIRLIN;OSIRIS;VeriEye;acute glaucoma;anterior synechiae;atrophy;cataract;corneal grafting;corneal ulcer;corneal vascularization;disease-affected eyes;distorted iris tissue;eye pathology;false nonmatch rate;geometrically distorted irides;haze;healthy eyes;illness;iris damage;iris image database;iris recognition reliability assessment;obstructed iris tissue;ocular disease;ocular pathology;opacity;posterior synechiae;retinal detachment;routine ophthalmology visit;rubeosis iridis;Biomedical imaging;Databases;Diseases;Geometry;Iris recognition;Lenses;Pathology;eye conditions;iris image databases;iris recognition;performance evaluation},

}

Adam Czajka, Kevin W. Bowyer, The 2nd IEEE International Conference on Cybernetics CYBCONF 2015, Special Session on Reliable Biometrics BIORELIABILITY 2015, June 24-26, 2015, Gdynia, Poland.

[pdf]

@InProceedings{Czajka_CYBCONF_2015,

  author    = {Adam Czajka and Kevin W. Bowyer},

  title     = {Statistical analysis of multiple presentation attempts in iris recognition},

  booktitle = {{IEEE} International Conference on Cybernetics (CYBCONF)},

  year      = {2015},

  pages     = {483-488},

  address   = {Gdynia, Poland},

  month     = {June},

  publisher = {IEEE},

  abstract  = {This paper presents experimental results showing uneven distributions of selected iris image quality metrics in the consecutive attempts in a biometric system that allows for multiple attempts to complete a transaction. We consider three iris image quality metrics that can be influenced by user behavior: usable iris area, motion blur and margin adequacy. The quality metrics are used to judge the overall quality of the iris image and accept or reject it on each attempt. The experiment simulates a typical physical access scenario with a maximum of three attempts in one transaction. One conclusion is that subjects rejected on the first attempt do, on average, improve the quality of their iris image on their second attempt. If their second image is rejected, the average quality improvement on the third attempt is lower. A second conclusion is that the probability of a subject being rejected on the second try is higher in average than the probability calculated for all subjects delivering their first samples. The latter finding contrasts with a common belief that each try in a single transaction can be assumed to be a draw from the same authentic distribution (and hence the rejection probabilities are equal in each try). A third interesting and surprising observation is that improvement of sample quality is higher for women than for men. To our knowledge, this paper presents the first research explaining the nature of multi-attempt iris recognition system and delivers conclusions that suggest that the default understanding of this process is too simplistic.},

  day       = {24--26},

  doi       = {10.1109/CYBConf.2015.7175982},

  keywords  = {image motion analysis;image restoration;iris recognition;probability;statistical analysis;biometric system;iris image quality metrics;iris recognition;margin adequacy;motion blur;multiple presentation attempts;rejection probabilities;statistical analysis;usable iris area;user behavior;Cameras;Image quality;Iris;Iris recognition;Measurement;Sociology;Statistics;iris image quality;iris recognition;multiple-attempt system;statistical analysis},

}

S. Banerjee, D. Mery, iPSIVT Workshop on 2D & 3D Geometric Properties from Incomplete Data (PSIVT-W), 2015.

[pdf] [code]

@InProceedings{10.1007/978-3-319-30285-0_5,

author="Banerjee, Sandipan

and Mery, Domingo",

editor="Huang, Fay

and Sugimoto, Akihiro",

title="Iris Segmentation Using Geodesic Active Contours and GrabCut",

booktitle="Image and Video Technology -- PSIVT 2015 Workshops",

year="2016",

publisher="Springer International Publishing",

address="Cham",

pages="48--60",

abstract="Iris segmentation is an important step in iris recognition as inaccurate segmentation often leads to faulty recognition. We propose an unsupervised, intensity based iris segmentation algorithm in this paper. The algorithm is fully automatic and can work for varied levels of occlusion, illumination and different shapes of the iris. A near central point inside the pupil is first detected using intensity based profiling of the eye image. Using that point as the center, we estimate the outer contour of the iris and the contour of the pupil using geodesic active contours, an iterative energy minimization algorithm based on the gradient of intensities. The iris region is then segmented out using both these estimations by applying an automatic version of GrabCut, an energy minimization algorithm from the graph cut family, representing the image as a Markov random field. The final result is refined using an ellipse-fitting algorithm based on the geometry of the GrabCut segmentation. To test our method, experiments were performed on 600 near infra-red eye images from the GFI database. The following features of the iris image are estimated: center and radius of the pupil and the iris. In order to evaluate the performance, we compare the features obtained by our method and the segmentation modules of three popular iris recognition systems with manual segmentation (ground truth). The results show that the proposed method performs as good as, in many cases better, when compared with these systems.",

isbn="978-3-319-30285-0"

}

Amanda Sgroi, Patrick Flynn, Kevin Bowyer and P. Jonathon Phillips, IEEE Transactions on Information Forensics and Security, 10 (6), 1207-1220, June 2015.

[pdf]

@ARTICLE{7039210, 

author={A. Sgroi and P. J. Flynn and K. Bowyer and P. J. Phillips}, 

journal={IEEE Transactions on Information Forensics and Security}, 

title={Strong, Neutral, or Weak: Exploring the Impostor Score Distribution}, 

year={2015}, 

volume={10}, 

number={6}, 

pages={1207-1220}, 

keywords={face recognition;fingerprint identification;image matching;iris recognition;SNoW concept;face recognition system;fingerprint recognition;impostor score distribution;iris recognition;partition stability;strong-neutral-weak face impostor pair problem;Algorithm design and analysis;Face;Face recognition;Partitioning algorithms;Probes;Snow;Standards;Biometrics;biometrics;face recognition;fingerprint recognition;iris recognition;performance evaluation}, 

doi={10.1109/TIFS.2015.2403136}, 

ISSN={1556-6013}, 

month={June},}

Estefan Ortiz and Kevin W. Bowyer, CVPR Biometrics Workshop, June 2015.

[pdf]

@InProceedings{Ortiz_2015_CVPR_Workshops,

author = {Ortiz, Estefan and Bowyer, Kevin W.},

title = {Exploratory Analysis of an Operational Iris Recognition Dataset From a CBSA Border-Crossing Application},

booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},

month = {June},

year = {2015}

}

Christopher Boehnen, David Bolme, Patrick Flynn, Biometric and Surveillance Technology for Human and Activity Identification XII, International Society for Optics and Photonics, vol. 9457, May 2015.

[link]

@proceeding{doi: 10.1117/12.2181981,

author = { Christopher  Boehnen,David  Bolme,Patrick  Flynn},

title = {Biometrics IRB best practices and data protection},

journal = {Proc.SPIE},

volume = {9457},

number = {},

pages = {9457 - 9457 - 7},

year = {2015},

doi = {10.1117/12.2181981},

URL = {https://doi.org/10.1117/12.2181981},

eprint = {}

}

Arun Ross, Michal Uricar, Vojtech Franc, Diego Thomas, Akihiro Sugimoto, Vaclav Hlavac, Amanda Sgroi, Hannah Garvey, Kevin Bowyer, Patrick Flynn, Stefanos Zafeiriou, Estefan Ortiz, Andrea Bottinok, Ihtesham Ullslam, Tiago Vieira, Simon Dobrisek, Vitomir Struc, Janez Krizaj, France Mihelic, Mehran Kafai, Kave Eshghi, Le An, Bir Bhanu, IEEE, May 2015.

@INPROCEEDINGS{7284808, 

author={}, 

booktitle={2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)}, 

title={B-WILD 2015: Biometrics in the Wild}, 

year={2015}, 

volume={02}, 

number={}, 

pages={1-2}, 

keywords={}, 

doi={10.1109/FG.2015.7284808}, 

ISSN={}, 

month={May},}

P. Jonathon Phillips, Patrick J. Flynn and Kevin W. Bowyer, IEEE International Conference on Automatic Face and Gesture Recognition (FG 2015), May 2015, Ljubljana, Slovenia.

[pdf]

@article{PHILLIPS201796,

title = "Lessons from collecting a million biometric samples",

journal = "Image and Vision Computing",

volume = "58",

pages = "96 - 107",

year = "2017",

issn = "0262-8856",

doi = "https://doi.org/10.1016/j.imavis.2016.08.004",

url = "http://www.sciencedirect.com/science/article/pii/S0262885616301287",

author = "P. Jonathon Phillips and Patrick J. Flynn and Kevin W. Bowyer",

keywords = "Face recognition, Algorithm performance, Human performance, Challenge problem"

}

Kevin W. Bowyer, Estefan Ortiz and Amanda Sgroi, Biometrics in the Wild Workshop 2015 (BWild 2015), May 2015, Ljubljana, Slovenia.

[pdf]

@INPROCEEDINGS{7284833, 

author={K. W. Bowyer and E. Ortiz and A. Sgroi}, 

booktitle={2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)}, 

title={Trial Somaliland voting register de-duplication using iris recognition}, 

year={2015}, 

volume={02}, 

number={}, 

pages={1-8}, 

keywords={government data processing;image classification;image matching;iris recognition;2010 Somaliland presidential election;forensic iris matching;image matching;iris recognition;trial Somaliland voting register deduplication;voter registration;Error analysis;Fingerprint recognition;Forensics;Iris;Iris recognition;Nominations and elections;Registers}, 

doi={10.1109/FG.2015.7284833}, 

ISSN={}, 

month={May},}

Amanda Sgroi, Patrick Flynn, Kevin W. Bowyer and Hannah Garvey, Biometrics in the Wild Workshop 2015 (BWild 2015), May 2015, Ljubljana, Slovenia.

[pdf]

@INPROCEEDINGS{7284812, 

author={A. Sgroi and H. Garvey and K. Bowyer and P. Flynn}, 

booktitle={2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)}, 

title={Location matters: A study of the effects of environment on facial recognition for biometric security}, 

year={2015}, 

volume={02}, 

number={}, 

pages={1-7}, 

keywords={face recognition;image matching;security of data;visual databases;FRVT 2006 dataset;acquisition locations;biometric security;cross-environment image matching;environment recognition;face recognition accuracy;gallery images;location matters;probe images;within-environment image matching;Accuracy;Face;Face recognition;Indoor environments;Lighting;Probes;Security}, 

doi={10.1109/FG.2015.7284812}, 

ISSN={}, 

month={May},}

Adam Czajka, IEEE Transactions on Information Forensics and Security, Vol. 10(4), pp. 726-735, April 2015.

[pdf]

@Article{Czajka_TIFS_2015,

  author    = {Adam Czajka},

  title     = {Pupil Dynamics for Iris Liveness Detection},

  journal   = {{IEEE} Transactions on Information Forensics and Security},

  year      = {2015},

  volume    = {10},

  number    = {4},

  pages     = {726-735},

  month     = {April},

  issn      = {1556-6013},

  abstract  = {The primary objective of this paper is to propose a complete methodology for eye liveness detection based on pupil dynamics. This method may serve as a component of presentation attack detection in iris recognition systems, making them more secure. Due to a lack of public databases that would support this paper, we have built our own iris capture device to register pupil size changes under visible light stimuli, and registered 204 observations for 26 subjects (52 different irides), each containing 750 iris images taken every 40 ms. Each measurement registers the spontaneous pupil oscillations and its reaction after a sudden increase of the intensity of visible light. The Kohn and Clynes pupil dynamics model is used to describe these changes; hence we convert each observation into a feature space defined by model parameters. To answer the question whether the eye is alive (that is, if it reacts to light changes as a human eye) or the presentation is suspicious (that is, if it reacts oddly or no reaction is observed), we use linear and nonlinear support vector machines to classify natural reaction and spontaneous oscillations, simultaneously investigating the goodness of fit to reject bad modeling. Our experiments show that this approach can achieve a perfect performance for the data we have collected. All normal reactions are correctly differentiated from spontaneous oscillations. We investigated the shortest observation time required to model the pupil reaction, and found that time periods not exceeding 3 s are adequate to offer a perfect performance.},

  comment   = {iris:PAD:dynamic:active},

  doi       = {10.1109/TIFS.2015.2398815},

  keywords  = {computer crime;feature extraction;image classification;iris recognition;support vector machines;eye liveness detection;feature space;iris capture device;iris images;iris liveness detection;iris recognition systems;model parameters;natural reaction classification;nonlinear support vector machines;presentation attack detection;pupil dynamics;pupil oscillations;pupil size changes;spontaneous oscillations;visible light intensity;visible light stimuli;Cameras;Databases;Iris recognition;Lenses;Motion pictures;Oscillators;Liveness detection;biometrics;iris recognition;presentation attack detection;pupil dynamics},

  publisher = {IEEE},

}

Christian Poellabauer, Nikhil Yadav, Louis Daudet, Sandra L Schneider, Carlos Busso, Patrick J Flynn, IEEE Access, IEEE, vol. 3, pp. 1143-1160, 2015.

[pdf]

@article{poellabauer2015challenges,

  title={Challenges in concussion detection using vocal acoustic biomarkers},

  author={Poellabauer, Christian and Yadav, Nikhil and Daudet, Louis and Schneider, Sandra L and Busso, Carlos and Flynn, Patrick J},

  journal={IEEE Access},

  volume={3},

  pages={1143--1160},

  year={2015},

  publisher={IEEE}

}

J Ross Beveridge, Hao Zhang, Bruce A Draper, Patrick J Flynn, Zhenhua Feng, Patrik Huber, Josef Kittler, Zhiwu Huang, Shaoxin Li, Yan Li, Meina Kan, Ruiping Wang, Shiguang Shan, Xilin Chen, Haoxiang Li, Gang Hua, Vitomir Struc, Janez Krizaj, Changxing Ding, Dacheng Tao, P Jonathon Phillips, IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), IEEE, 2015.

[pdf]

@inproceedings{beveridge2015report,

  title={Report on the FG 2015 video person recognition evaluation},

  author={Beveridge, J Ross and Zhang, Hao and Draper, Bruce A and Flynn, Patrick J and Feng, Zhenhua and Huber, Patrik and Kittler, Josef and Huang, Zhiwu and Li, Shaoxin and Li, Yan and others},

  booktitle={IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)},

  year={2015},

  organization={IEEE}

}

2014

Amanda Sgroi, Kevin Bowyer, Patrick Flynn, Information Forensics and Security (WIFS), 2014 IEEE International Workshop on, IEEE, pp. 7-12, December 2014.

[pdf]

@INPROCEEDINGS{7084295, 

author={A. Sgroi and K. Bowyer and P. Flynn}, 

booktitle={2014 IEEE International Workshop on Information Forensics and Security (WIFS)}, 

title={Metadata-based understanding of impostor pair score variations}, 

year={2014}, 

volume={}, 

number={}, 

pages={7-12}, 

keywords={face recognition;image matching;meta data;SNoW technique;face image;impostor image pair;impostor pair score variation;matching metadata value;metadata matches;metadata-based understanding;probe images;Algorithm design and analysis;Face;Face recognition;Image recognition;Market research;Partitioning algorithms;Snow;biometrics;face recognition;metadata;score analysis}, 

doi={10.1109/WIFS.2014.7084295}, 

ISSN={2157-4766}, 

month={Dec},}

Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz, Proc. SPIE 9290, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2014, Vol. 9290, November 25, 2014.

[pdf]

@InProceedings{Trokielewicz_SPIE_2014,

  author    = {Mateusz Trokielewicz and Adam Czajka and Piotr Maciejewicz},

  title     = {Cataract influence on iris recognition performance},

  booktitle = {Proceedings of SPIE},

  year      = {2014},

  volume    = {9290},

  pages     = {9290-9290-14},

  month     = {November},

  abstract  = {This paper presents the experimental study revealing weaker performance of the automatic iris recognition methods for cataract-affected eyes when compared to healthy eyes. There is little research on the topic, mostly incorporating scarce databases that are often deficient in images representing more than one illness. We built our own database, acquiring 1288 eye images of 37 patients of the Medical University of Warsaw. Those images represent several common ocular diseases, such as cataract, along with less ordinary conditions, such as iris pattern alterations derived from illness or eye trauma. Images were captured in near-infrared light (used in biometrics) and for selected cases also in visible light (used in ophthalmological diagnosis). Since cataract is a disorder that is most populated by samples in the database, in this paper we focus solely on this illness. To assess the extent of the performance deterioration we use three iris recognition methodologies (commercial and academic solutions) to calculate genuine match scores for healthy eyes and those influenced by cataract. Results show a significant degradation in iris recognition reliability manifesting by worsening the genuine scores in all three matchers used in this study (12% of genuine score increase for an academic matcher, up to 175% of genuine score increase obtained for an example commercial matcher). This increase in genuine scores affected the final false non-match rate in two matchers. To our best knowledge this is the only study of such kind that employs more than one iris matcher, and analyzes the iris image segmentation as a potential source of decreased reliability},

  day       = {25},

  doi       = {10.1117/12.2076040},

}

Adam Czajka, Communications in Computer and Information Science, Volume 452, pp. 284-299, Springer, November 2014.

[pdf]

@InCollection{Czajka_CCIS_2014,

  author    = {Adam Czajka},

  title     = {Influence of Iris Template Aging on Recognition Reliability},

  booktitle = {Communications in Computer and Information Science},

  publisher = {Springer},

  year      = {2014},

  volume    = {452},

  pages     = {284-299},

  abstract  = {The paper presents an iris aging analysis based on comparison results obtained for four different iris matchers. We collected an iris aging database of samples captured even eight years apart. To our best knowledge, this is the only database worldwide of iris images collected with such a large time distance between capture sessions. We evaluated the influence of the intra- vs. inter-session accuracy of the iris recognition, as well as the accuracy between the short term (up to two years) vs. long term comparisons (from 5 to 9 years). The average genuine scores revealed statistically significant differences with respect to the time distance between examined samples (up to 14 % of degradation in the average genuine scores is observed). These results may suggest that the iris pattern ages to some extent, and thus appropriate countermeasures should be deployed in application assuming large time distances between iris template replacements (or adaptations).},

  doi       = {https://doi.org/10.1007/978-3-662-44485-6\_20},

  keywords  = {biometric template aging;Iris recognition;biometrics},

}

Jeremiah R. Barr, Kevin W. Bowyer and Patrick J. Flynn, IEEE Transactions on Information Forensics and Security 9 (11), 1986-2001, November 2014.

[pdf]

@ARTICLE{6905835, 

author={J. R. Barr and K. W. Bowyer and P. J. Flynn}, 

journal={IEEE Transactions on Information Forensics and Security}, 

title={Framework for Active Clustering With Ensembles}, 

year={2014}, 

volume={9}, 

number={11}, 

pages={1986-2001}, 

keywords={Clustering algorithms;Face;Face recognition;Labeling;Measurement;Noise;Partitioning algorithms}, 

doi={10.1109/TIFS.2014.2359369}, 

ISSN={1556-6013}, 

month={Nov},

}

Nikhil Yadav, Louis Daudet, Christian Poellabauer, Patrick Flynn, Healthcare Innovation Conference (HIC), IEEE, pp. 335-338, October 2014.

[pdf]

@INPROCEEDINGS{7038943, 

author={N. Yadav and L. Daudet and C. Poellabauer and P. Flynn}, 

booktitle={2014 IEEE Healthcare Innovation Conference (HIC)}, 

title={Noise management in mobile speech based health tools}, 

year={2014}, 

volume={}, 

number={}, 

pages={335-338}, 

keywords={health care;medical disorders;microphones;smart phones;speech;speech recognition;telemedicine;acoustic environment;microphone;mobile devices;mobile health tools;mobile speech;noise management;signal-to-noise ratio;speech disorder detector;speech processing capabilities;speech recognition accuracy;word accuracy;Accuracy;Acoustics;Mobile communication;Signal to noise ratio;Speech;Speech recognition}, 

doi={10.1109/HIC.2014.7038943}, 

ISSN={}, 

month={Oct},}

David Yambay, James Doyle, Kevin Bowyer, Adam Czajka, Stephanie Schuckers, International Joint Conference on Biometrics - IJCB 2014, September 29 - October 2, 2014, Clearwater, Florida, USA.

[pdf]

@InProceedings{Yambay_IJCB_2014,

  author    = {David Yambay and James S. Doyle and Kevin W. Bowyer and Adam Czajka and Stephanie Schuckers},

  title     = {LivDet-iris 2013 - Iris Liveness Detection Competition 2013},

  booktitle = {{IEEE} International Joint Conference on Biometrics (IJCB)},

  address   = {Clearwater, FL, USA},

  publisher = {IEEE},

  year      = {2014},

  pages     = {1-8},

  month     = {Sept},

  abstract  = {The use of an artificial replica of a biometric characteristic in an attempt to circumvent a system is an example of a biometric presentation attack. Liveness detection is one of the proposed countermeasures, and has been widely implemented in fingerprint and iris recognition systems in recent years to reduce the consequences of spoof attacks. The goal for the Liveness Detection (LivDet) competitions is to compare software-based iris liveness detection methodologies using a standardized testing protocol and large quantities of spoof and live images. Three submissions were received for the competition Part 1; Biometric Recognition Group de Universidad Autonoma de Madrid, University of Naples Federico II, and Faculdade de Engenharia de Universidade do Porto. The best results from across all three datasets was from Federico with a rate of falsely rejected live samples of 28.6% and the rate of falsely accepted fake samples of 5.7%.},

  comment   = {iris:PAD},

  doi       = {10.1109/BTAS.2014.6996283},

  keywords  = {iris recognition;Biometric Recognition Group de Universidad Autonoma de Madrid;Faculdade de Engenharia de Universidade do Porto;Iris Liveness Detection Competition 2013;LivDet-iris 2013;University of Naples Federico II;biometric characteristic;biometric presentation attack;fingerprint recognition system;iris recognition system;software-based iris liveness detection methodologies;spoof attacks;standardized testing protocol;Cameras;Educational institutions;Iris;Iris recognition;Lenses;Testing;Training},

}

Estefan Ortiz, Kevin W Bowyer, Patrick J Flynn, Biometrics (IJCB), 2014 IEEE International Joint Conference on, IEEE, pp. 1-6, September 2014.

[link]

@INPROCEEDINGS{6996297, 

author={E. Ortiz and K. W. Bowyer and P. J. Flynn}, 

booktitle={IEEE International Joint Conference on Biometrics}, 

title={An optimal strategy for dilation based iris image enrollment}, 

year={2014}, 

volume={}, 

number={}, 

pages={1-6}, 

keywords={approximation theory;image matching;iris recognition;approximate linear relationship;iris biometrics;iris image enrollment;matching errors;optimal strategy;pupil dilation;Analytical models;Equations;Iris;Iris recognition;Mathematical model;NIST;Probes}, 

doi={10.1109/BTAS.2014.6996297}, 

ISSN={}, 

month={Sept},}

Juan E. Tapia, Claudio A. Perez and Kevin W. Bowyer, ECCV Workshop on Soft Biometrics, September 2014.

[pdf]

@InProceedings{10.1007/978-3-319-16181-5_57,

author="Tapia, Juan E.

and Perez, Claudio A.

and Bowyer, Kevin W.",

editor="Agapito, Lourdes

and Bronstein, Michael M.

and Rother, Carsten",

title="Gender Classification from Iris Images Using Fusion of Uniform Local Binary Patterns",

booktitle="Computer Vision - ECCV 2014 Workshops",

year="2015",

publisher="Springer International Publishing",

address="Cham",

pages="751--763",

abstract="This paper is concerned in analyzing iris texture in order to determine ``soft biometric'', attributes of a person, rather than identity. In particular, this paper is concerned with predicting the gender of a person based on analysis of features of the iris texture. Previous researchers have explored various approaches for predicting the gender of a person based on iris texture. We explore using different implementations of Local Binary Patterns from the iris image using the masked information. Uniform LBP with concatenated histograms significantly improves accuracy of gender prediction relative to using the whole iris image. Using a subject-disjoint test set, we are able to achieve over 91 {\%} correct gender prediction using the texture of the iris. To our knowledge, this is the highest accuracy yet achieved for predicting gender from iris texture.",

isbn="978-3-319-16181-5"

}

Domingo Mery and Kevin Bowyer, ECCV Workshop on Soft Biometrics, September 2014.

[pdf] [best paper award]

@InProceedings{10.1007/978-3-319-16181-5_59,

author="Mery, Domingo

and Bowyer, Kevin",

editor="Agapito, Lourdes

and Bronstein, Michael M.

and Rother, Carsten",

title="Recognition of Facial Attributes Using Adaptive Sparse Representations of Random Patches",

booktitle="Computer Vision - ECCV 2014 Workshops",

year="2015",

publisher="Springer International Publishing",

address="Cham",

pages="778--792",

abstract="It is well known that some facial attributes --like soft biometric traits-- can increase the performance of traditional biometric systems and help recognition based on human descriptions. In addition, other facial attributes --like facial expressions-- can be used in human--computer interfaces, image retrieval, talking heads and human emotion analysis. This paper addresses the problem of automated recognition of facial attributes by proposing a new general approach called Adaptive Sparse Representation of Random Patches (ASR+). In the learning stage, random patches are extracted from representative face images of each class (e.g., in gender recognition --a two-class problem--, images of females/males) in order to construct representative dictionaries. In the testing stage, random test patches of the query image are extracted, and for each test patch a dictionary is built concatenating the `best' representative dictionary of each class. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification (SRC) methodology. Finally, the query image is classified by patch voting. Thus, our approach is able to learn a model for each recognition task dealing with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size and distance from the camera. Experiments were carried out on seven face databases in order to recognize facial expression, gender, race and disguise. Results show that ASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature in many complex scenarios.",

isbn="978-3-319-16181-5"

}

P J Phillips, JR Beveridge, Hao Zhang, Patrick J Flynn, Venice E Liong, Jiwen Lu, Marcus de Assis Angeloni, Tiago de Freitas Pereira, Haoxiang Li, Vitomir Struc, Janez Krizaj, International Joint Conference on Biometrics (IJCB 2014), August 2014.

[pdf]

@INPROCEEDINGS{6996256, 

author={J. R. Beveridge and H. Zhang and P. J. Flynn and Y. Lee and V. E. Liong and J. Lu and M. de Assis Angeloni and T. de Freitas Pereira and H. Li and G. Hua and V. Štruc and J. Križaj and P. J. Phillips}, 

booktitle={IEEE International Joint Conference on Biometrics}, 

title={The IJCB 2014 PaSC video face and person recognition competition}, 

year={2014}, 

volume={}, 

number={}, 

pages={1-8}, 

keywords={cameras;face recognition;pose estimation;query processing;sensitivity analysis;video signal processing;Advanced Digital Science Center;Brasil;CPqD;IJCB PaSC video face-and-person recognition competition;Point-and-Shoot Face Recognition Challenge;ROC curves;Singapore;Slovenia;Stevens Institute of Technology;USA;University of Ljubljana;algorithm performance characterization;eye coordinates;false accept rate;handheld cameras;nonfrontal head pose;performance evaluation challenge;query video;receiver operating characteristic curves;still images;still-to-video experiment;target videos;verification rate;video frames;video-to-video Experiment;Cameras;Educational institutions;Face;Face recognition;Feature extraction;Histograms;Vectors}, 

doi={10.1109/BTAS.2014.6996256}, 

ISSN={}, 

month={Sept},}

Jim Thomas, Ahsan Kareem and Kevin W. Bowyer, IEEE Transactions on Geoscience and Remote Sensing 52 (7), 3851-3861, July 2014.

[pdf]

@ARTICLE{6644293, 

author={J. Thomas and A. Kareem and K. W. Bowyer}, 

journal={IEEE Transactions on Geoscience and Remote Sensing}, 

title={Automated Poststorm Damage Classification of Low-Rise Building Roofing Systems Using High-Resolution Aerial Imagery}, 

year={2014}, 

volume={52}, 

number={7}, 

pages={3851-3861}, 

keywords={edge detection;feature extraction;geophysical image processing;image classification;remote sensing;automated poststorm damage classification;collapsed rooftop;color-based feature;detailed damage assessment;edge-based feature;high-resolution aerial imagery;intensity-based feature;low-rise building roofing systems;postdisaster assessment;posthurricane damage estimation;remotely sensed images;utilizing supervised classification;Buildings;Correlation;Feature extraction;Histograms;Hurricanes;Image color analysis;Image edge detection;Aerial image hurricane disaster assessments;emergency response planning;supervised damage classification}, 

doi={10.1109/TGRS.2013.2277092}, 

ISSN={0196-2892}, 

month={July},}

Daksha Yadav, Naman Kohli, J.S. Doyle, Richa Singh, Mayank Vatsa, Kevin W. Bowyer, IEEE Trans. Inf. Forens. Security, vol. 9, pp. 851 - 862, May 2014.

[pdf]

@ARTICLE{6776569, 
author={D. Yadav and N. Kohli and J. S. Doyle and R. Singh and M. Vatsa and K. W. Bowyer}, 
journal={IEEE Transactions on Information Forensics and Security}, 
title={Unraveling the Effect of Textured Contact Lenses on Iris Recognition}, 
year={2014}, 
volume={9}, 
number={5}, 
pages={851-862}, 
keywords={image texture;iris recognition;visual databases;IIIT-D iris contact lens database;ND contact lens database;iris recognition;textured contact lenses;textured cosmetic lens;Databases;Image color analysis;Iris;Iris recognition;Lenses;Probes;Training;Iris recognition;contact lens;lens detection}, 
doi={10.1109/TIFS.2014.2313025}, 
ISSN={1556-6013}, 
month={May},

}

Kevin W. Bowyer and James S. Doyle, IEEE Computer 47 (5), 96-98, May 2014.

[pdf]

@ARTICLE{6818913, 

author={K. W. Bowyer and J. S. Doyle}, 

journal={Computer}, 

title={Cosmetic Contact Lenses and Iris Recognition Spoofing}, 

year={2014}, 

volume={47}, 

number={5}, 

pages={96-98}, 

keywords={contact lenses;iris recognition;object detection;automatic cosmetic contact lense detection;iris images;iris recognition spoofing;pattern-recognition problem;Image color analysis;Iris recognition;Lenses;Lighting;Pigments;Sensors;bioinformatics;biometrics;cosmetic contact lenses;identity sciences;iris recognition;near-infrared illumination;pattern recognition;security;spoofing}, 

doi={10.1109/MC.2014.118}, 

ISSN={0018-9162}, 

month={May},}

Jeremiah R Barr, Kevin W Bowyer, Patrick J Flynn, Applications of Computer Vision (WACV), 2014 IEEE Winter Conference on, IEEE, pp. 1020-1027, March 2014.

[pdf]

@INPROCEEDINGS{6835992, 

author={J. R. Barr and K. W. Bowyer and P. J. Flynn}, 

booktitle={IEEE Winter Conference on Applications of Computer Vision}, 

title={The effectiveness of face detection algorithms in unconstrained crowd scenes}, 

year={2014}, 

volume={}, 

number={}, 

pages={1020-1027}, 

keywords={face recognition;feature extraction;object detection;Boston Marathon crowds;FDDB;Viola-Jones algorithm;face detection algorithms;face detection data set and benchmark;multipose generalization;unconstrained crowd scenes;Approximation algorithms;Benchmark testing;Cameras;Detectors;Face;Face detection;Feature extraction}, 

doi={10.1109/WACV.2014.6835992}, 

ISSN={1550-5790}, 

month={March},}

Min-Ki Kim, Patrick J Flynn, Applications of Computer Vision (WACV), 2014 IEEE Winter Conference on, IEEE, pp. 992-997, March 2014.

[pdf]

@INPROCEEDINGS{6835996, 

author={M. K. Kim and P. J. Flynn}, 

booktitle={IEEE Winter Conference on Applications of Computer Vision}, 

title={Finger-knuckle-print verification based on vector consistency of corresponding interest points}, 

year={2014}, 

volume={}, 

number={}, 

pages={992-997}, 

keywords={correlation methods;fingerprint identification;gradient methods;image matching;transforms;vectors;CIP;FKP verification method;SIFT feature;corresponding interest point;finger images;finger-knuckle-print verification;gradient directionality;impostor match;intensity field;matching score;phase correlation;vector consistency;Biometrics (access control);Correlation;Equations;Fingers;Gabor filters;Histograms;Vectors}, 

doi={10.1109/WACV.2014.6835996}, 

ISSN={1550-5790}, 

month={March},}

Jeremiah R Barr, Leonardo A Cament, Kevin W Bowyer, Patrick J Flynn, Applications of Computer Vision (WACV), 2014 IEEE Winter Conference on, IEEE, pp. 969-976, March 2014.

[pdf]

@INPROCEEDINGS{6835999, 

author={J. R. Barr and L. A. Cament and K. W. Bowyer and P. J. Flynn}, 

booktitle={IEEE Winter Conference on Applications of Computer Vision}, 

title={Active Clustering with Ensembles for Social structure extraction}, 

year={2014}, 

volume={}, 

number={}, 

pages={969-976}, 

keywords={feature extraction;network analysis;pattern clustering;social networking (online);video on demand;active clustering;identity cluster;matched faces;network analysis techniques;social network structure;social structure extraction;video clips;video frames;Algorithm design and analysis;Bridges;Clustering algorithms;Communities;Face recognition;Partitioning algorithms;Social network services}, 

doi={10.1109/WACV.2014.6835999}, 

ISSN={1550-5790}, 

month={March},}

Feng Shen, Patrick J Flynn, 2014 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 977-983, March 2014.

[pdf]

@INPROCEEDINGS{6835998, 

author={F. Shen and P. J. Flynn}, 

booktitle={IEEE Winter Conference on Applications of Computer Vision}, 

title={Iris crypts: Multi-scale detection and shape-based matching}, 

year={2014}, 

volume={}, 

number={}, 

pages={977-983}, 

keywords={cryptography;feature extraction;image coding;image matching;optimisation;feature candidate detection;heuristic-based optimization;iris crypt detection;multiscale detection;multiscale pyramid architecture;shape descriptors;shape-based matching;Cryptography;Feature extraction;Image edge detection;Integrated circuits;Iris recognition;Shape}, 

doi={10.1109/WACV.2014.6835998}, 

ISSN={1550-5790}, 

month={March},}

J. Paone, P. Flynn, P.J. Phillips, K. Bowyer, R. Vorder Bruegge, P. Grother, G. Quinn, M. Pruitt, J. Grant, IEEE Trans. Inf. Forens. Security, vol. 9, pp. 285 - 295, Feb. 2014.

[pdf]

@ARTICLE{6693698, 

author={J. R. Paone and P. J. Flynn and P. J. Philips and K. W. Bowyer and R. W. V. Bruegge and P. J. Grother and G. W. Quinn and M. T. Pruitt and J. M. Grant}, 

journal={IEEE Transactions on Information Forensics and Security}, 

title={Double Trouble: Differentiating Identical Twins by Face Recognition}, 

year={2014}, 

volume={9}, 

number={2}, 

pages={285-295}, 

keywords={biometrics (access control);face recognition;face recognition;gesture recognition;identical twins;Algorithm design and analysis;Cameras;Face;Face recognition;Lighting;Sociology;Statistics;Face and gesture recognition}, 

doi={10.1109/TIFS.2013.2296373}, 

ISSN={1556-6013}, 

month={Feb},

}

2013

Soma Biswas, Gaurav Aggarwal, Patrick J. Flynn and Kevin W. Bowyer, IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (12), 3037-3049, December 2013.

[pdf]

@ARTICLE{6494574, 

author={S. Biswas and G. Aggarwal and P. J. Flynn and K. W. Bowyer}, 

journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 

title={Pose-Robust Recognition of Low-Resolution Face Images}, 

year={2013}, 

volume={35}, 

number={12}, 

pages={3037-3049}, 

keywords={image classification;image matching;image recognition;object tracking;pose estimation;tensors;video surveillance;MultiPIE dataset;classifier-based approaches;face matching algorithms;facial landmark localization;frontal pose images;high-quality gallery images;high-resolution images;low-resolution face images;low-resolution uncontrolled probe images;multidimensional scaling;poor quality probe images;pose-robust recognition;super-resolution;surveillance cameras;surveillance imagery;surveillance quality facial image matching;surveillance video recognition;surveillance video tracking;tensor analysis;Cameras;Facial recognition;Iterative methods;Resolution;Surveillance;Face recognition;iterative majorization;low-resolution matching;multidimensional scaling;0}, 

doi={10.1109/TPAMI.2013.68}, 

ISSN={0162-8828}, 

month={Dec},}

Feng Shen, Patrick J Flynn, Biometrics: Theory, Applications and Systems (BTAS), 2013 IEEE Sixth International Conference on, IEEE, pp. 1-6, September 2013.

[pdf]

@INPROCEEDINGS{6712722, 

author={F. Shen and P. J. Flynn}, 

booktitle={2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)}, 

title={Are iris crypts useful in identity recognition?}, 

year={2013}, 

volume={}, 

number={}, 

pages={1-6}, 

keywords={cryptography;image forensics;iris recognition;adopted matcher;crypt pixels;forensic applications;genuine similarity scores;identity recognition;interparticipant annotation consistency;intra-iris crypt perception;iris crypts;iris images;query images;Cryptography;Feature extraction;Forensics;Inspection;Integrated circuits;Iris recognition;Software}, 

doi={10.1109/BTAS.2013.6712722}, 

ISSN={}, 

month={Sept},}

Adam Czajka, The 18th International Conference on Methods and Models in Automation and Control (MMAR2013), August 26-29, 2013, Miedzyzdroje, Poland.

[pdf]

@InProceedings{Czajka_ICMMAR_2013,

  author    = {Adam Czajka},

  title     = {Database of iris printouts and its application: Development of liveness detection method for iris recognition},

  booktitle = {{IEEE} International Conference on Methods and Models in Automation and Robotics (MMAR)},

  publisher = {IEEE},

  address   = {Mi\c{e}dzyzdroje, Poland},

  year      = {2013},

  pages     = {28-33},

  month     = {August},

  abstract  = {Liveness detection (often referred to as presentation attack detection) is the ability to detect artificial objects presented to a biometric device with an intention to subvert the recognition system. This paper presents the database of iris printout images with a controlled quality, and its fundamental application, namely development of liveness detection method for iris recognition. The database gathers images of only those printouts that were accepted by an example commercial camera, i.e. the iris template calculated for an artefact was matched to the corresponding iris reference of the living eye. This means that the quality of the employed imitations is not accidental and precisely controlled. The database consists of 729 printout images for 243 different eyes, and 1274 images of the authentic eyes, corresponding to imitations. It may thus serve as a good benchmark for at least two challenges: a) assessment of the liveness detection algorithms, and b) assessment of the eagerness of matching real and fake samples by iris recognition methods. To our best knowledge, the iris printout database of such properties is the first worldwide published as of today. In its second part, the paper presents an example application of this database, i.e. the development of liveness detection method based on iris image frequency analysis. We discuss how to select frequency windows and regions of interest to make the method sensitive to “alien frequencies” resulting from the printing process. The proposed method shows a very promising results, since it may be configured to achieve no false alarms when the rate of accepting the iris printouts is approximately 5% (i.e. 95% of presentation attack trials are correctly identified). This favorable compares to the results of commercial equipment used in the database development, as this device accepted all the printouts used. The method employs the same image as used in iris recognition process, hence no investments into the capture devices is required, and may be applied also to other carriers for printed iris patterns, e.g. contact lens.},

  comment   = {iris:PAD:static:pasive},

  doi       = {10.1109/MMAR.2013.6669876},

  keywords  = {image matching;iris recognition;object detection;artificial object detection;authentic eyes;biometric device;database development;eagerness assessment;fake sample matching;frequency windows;image matching;iris image frequency analysis;iris printout database;iris printout images;iris recognition;liveness detection method;presentation attack detection;presentation attack trials;printing process;real sample matching;Cameras;Databases;Image resolution;Image segmentation;Iris;Iris recognition;Printers},

}

Patrick J Flynn, Anil K Jain, NCJRS, August 2013.

[pdf]

@article{flynn2013face,

  title={Face Annotation at the Macro-scale and the Micro-scale: Tools, Techniques, and Applications in Forensic Identification},

  author={Flynn, Patrick J and Jain, Anil K},

  journal={NCJRS, Aug},

  year={2013}

}

Adam Czajka, Paweł Bulwan, The 6th IAPR International Conference on Biometrics ICB-2013, June 4-7, 2013, Madrid, Spain.

[pdf]

@InProceedings{Czajka_ICB_2013,

  author    = {A. Czajka and P. Bulwan},

  title     = {Biometric verification based on hand thermal images},

  booktitle = {{IEEE} International Conference on Biometrics (ICB)},

  year      = {2013},

  pages     = {1-6},

  address   = {Madrid, Spain},

  month     = {June},

  publisher = {IEEE},

  abstract  = {The paper presents a biometric recognition methodology based on hand thermal information. We start with a hardware presentation, specially designed for this research in a form of thermal sensor plate delivering hand thermal maps, which is a significantly cheaper alternative to thermal cameras. We use a heuristic feature selection technique employing mutual information (mRMR) and well known space transformation methods (PCA and its combination with the LDA) to develop optimal biometric features by selecting those parts of the hand, which deliver the most discriminating personal information. Two different classifiers (k-NN and SVM) are applied and evaluated with a database of hand thermal maps captured for 50 different individuals in three sessions: two at the same day (enrollment attempts), and the third captured a week apart (verification attempt). We achieved 6.67% of an average equal error rate (EER), what suggests that temperature distribution of an inner part of human hand is individual. This may serve as e.g. supporting modality of two-modal biometric recognition (merged with hand geometry or palm print techniques), or may be a good candidate for hand liveness detection approach, as hand thermal maps are difficult to be copied and reconstructed on an artificial object imitating a human hand. To our best knowledge, this is the first work presenting the use of a human hand thermal maps as a direct source of biometric features.},

  day       = {4-7},

  doi       = {10.1109/ICB.2013.6612982},

  issn      = {2376-4201},

  keywords  = {cameras;image classification;infrared imaging;palmprint recognition;support vector machines;temperature distribution;EER;LDA;PCA;SVM;artificial object;average equal error rate;biometric recognition methodology;biometric verification;hand liveness detection approach;hand thermal images;hand thermal information;hand thermal maps;heuristic feature selection technique;k-NN classifiers;mRMR;mutual information;optimal biometric features;personal information;space transformation methods;temperature distribution;thermal cameras;thermal sensor plate;two-modal biometric recognition modality;Biomedical imaging;Databases;Error analysis;Principal component analysis;Support vector machines;Temperature sensors},

}

Amanda Sgroi, Kevin W. Bowyer and Patrick J. Flynn, IAPR International Conference on Biometrics, June 2013.

[pdf]

@INPROCEEDINGS{6612975, 

author={A. Sgroi and K. W. Bowyer and P. J. Flynn}, 

booktitle={2013 International Conference on Biometrics (ICB)}, 

title={The impact of diffuse illumination on iris recognition}, 

year={2013}, 

volume={}, 

number={}, 

pages={1-7}, 

keywords={image matching;iris recognition;lighting;diffuse illumination system;iris algorithms;iris illumination;iris matching;iris recognition performance;lighting variation;pupil;specular highlighting;Bars;Image segmentation;Iris;Iris recognition;Lenses;Light emitting diodes;Lighting}, 

doi={10.1109/ICB.2013.6612975}, 

ISSN={2376-4201}, 

month={June},}

Amanda Sgroi, Kevin W. Bowyer and Patrick J. Flynn, IAPR International Conference on Biometrics, June 2013.

[pdf]

@INPROCEEDINGS{6613010, 

author={A. Sgroi and K. W. Bowyer and P. J. Flynn}, 

booktitle={2013 International Conference on Biometrics (ICB)}, 

title={The prediction of old and young subjects from iris texture}, 

year={2013}, 

volume={}, 

number={}, 

pages={1-5}, 

keywords={image representation;image texture;iris recognition;ethnicity;gender;image representation;iris texture images;soft biometric attributes;Aging;Estimation;Face;Feature extraction;Iris;Iris recognition;Testing}, 

doi={10.1109/ICB.2013.6613010}, 

ISSN={2376-4201}, 

month={June},}

James S. Doyle, Patrick J. Flynn and Kevin W. Bowyer, IAPR International Conference on Biometrics, June 2013. 

[pdf]

@INPROCEEDINGS{6612954, 

author={J. S. Doyle and P. J. Flynn and K. W. Bowyer}, 

booktitle={2013 International Conference on Biometrics (ICB)}, 

title={Automated classification of contact lens type in iris images}, 

year={2013}, 

volume={}, 

number={}, 

pages={1-6}, 

keywords={contact lenses;image classification;image texture;iris recognition;automated classification;iris images;iris recognition systems;prescription lens;soft contact lenses;textured cosmetic contact lens;Accuracy;Feature extraction;Iris;Iris recognition;Lenses;Training}, 

doi={10.1109/ICB.2013.6612954}, 

ISSN={2376-4201}, 

month={June},}

Karen Hollingsworth, Samuel Clark, Joseph Thompson, Patrick J Flynn, Kevin W Bowyer, Biometric and Surveillance Technology for Human and Activity Identification X, International Society for Optics and Photonics, vol. 8712, May 2013.

[pdf]

@proceeding{doi: 10.1117/12.2017646,

author = { Karen  Hollingsworth,Samuel  Clark,Joseph  Thompson,Patrick J. Flynn,Kevin W. Bowyer},

title = {Eyebrow segmentation using active shape models},

journal = {Proc.SPIE},

volume = {8712},

number = {},

pages = {8712 - 8712 - 8},

year = {2013},

doi = {10.1117/12.2017646},

URL = {https://doi.org/10.1117/12.2017646},

eprint = {}

}

Feng Shen, Patrick J Flynn, Biometric and Surveillance Technology for Human and Activity Identification X, International Society for Optics and Photonics, vol. 8712, May 2013.

[pdf]

@proceeding{doi: 10.1117/12.2017931,

author = { Feng  Shen,Patrick J. Flynn},

title = {Using crypts as iris minutiae},

journal = {Proc.SPIE},

volume = {8712},

number = {},

pages = {8712 - 8712 - 8},

year = {2013},

doi = {10.1117/12.2017931},

URL = {https://doi.org/10.1117/12.2017931},

eprint = {}

}

James S Doyle, Patrick J Flynn, Kevin W Bowyer, Biometric and Surveillance Technology for Human and Activity Identification X, International Society for Optics and Photonics, vol. 8712, May 2013.

[pdf]

@proceeding{doi: 10.1117/12.2017877,

author = { James S. Doyle,Patrick J. Flynn,Kevin W. Bowyer},

title = {Effects of mascara on iris recognition},

journal = {Proc.SPIE},

volume = {8712},

number = {},

pages = {8712 - 8712 - 11},

year = {2013},

doi = {10.1117/12.2017877},

URL = {https://doi.org/10.1117/12.2017877},

eprint = {}

}

Michael Falcone, Nikhil Yadav, Christian Poellabauer, Patrick Flynn, Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, IEEE, pp. 7577-7581, May 2013.

[pdf]

@INPROCEEDINGS{6639136, 

author={M. Falcone and N. Yadav and C. Poellabauer and P. Flynn}, 

booktitle={2013 IEEE International Conference on Acoustics, Speech and Signal Processing}, 

title={Using isolated vowel sounds for classification of Mild Traumatic Brain Injury}, 

year={2013}, 

volume={}, 

number={}, 

pages={7577-7581}, 

keywords={bioelectric potentials;brain;feature extraction;health care;injuries;learning (artificial intelligence);medical signal detection;speech;speech processing;sport;Vowel sound isolation;acoustic feature extraction;athlete;boxing tournament;mild traumatic brain injury classification;mild traumatic brain injury detection;mobile device;one-class machine learning algorithm;speech analysis;sport;Accuracy;Acoustics;Brain injuries;Feature extraction;Jitter;Speech;Speech analysis;concussion;health and safety;predictive models}, 

doi={10.1109/ICASSP.2013.6639136}, 

ISSN={1520-6149}, 

month={May},}

Samuel P. Fenker, Estefan Ortiz and Kevin W. Bowyer, IEEE Access 1, 266-274, May 16, 2013.

[pdf]

@ARTICLE{6516567, 

author={S. P. Fenker and E. Ortiz and K. W. Bowyer}, 

journal={IEEE Access}, 

title={Template Aging Phenomenon in Iris Recognition}, 

year={2013}, 

volume={1}, 

number={}, 

pages={266-274}, 

keywords={image matching;iris recognition;age-related change;authentic distribution;biometric template aging;eye aging;false nonmatch rate;identity verification applications;iris recognition;pupil dilation;template aging phenomenon;Aging;Biomedical monitoring;Biometrics;Cornea;Error probablity;Iris recognition;Magnetic resonance;Biometrics;error probability;false non-match rate;iris recognition;template aging}, 

doi={10.1109/ACCESS.2013.2262916}, 

ISSN={}, 

month={},

}

Adam Czajka, 6th International Conference on Bio-Inspired Systems and Signal Processing, February 11-14, 2013, Barcelona, Spain.

[pdf]

@Conference{Czajka_BIOSIGNALS_2013,

  author       = {Adam Czajka},

  title        = {{Template Ageing in Iris Recognition}},

  booktitle    = {Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2013)},

  year         = {2013},

  pages        = {70-78},

  address      = {Barcelona, Spain},

  month        = {February},

  organization = {INSTICC},

  publisher    = {SciTePress},

  abstract     = {The paper presents an iris ageing analysis based on comparison results obtained for three different iris matchers (two of them not used earlier in papers devoted to iris template ageing). For the purpose of this research we collected an iris ageing database of samples captured even eight years apart. To our best knowledge, this is the only database of iris images collected with such a large time distance between capture sessions worldwide. We evaluated the influence of the intra- vs. inter-session accuracy of the iris recognition, as well as the accuracy between the short term (up to two years) vs. long term comparisons (from 5 to 9 years). The average genuine scores revealed statistically significant differences with respect to the time distance between examined samples (depending on the coding method, we obtained from 3% to 14% of degradation of the genuine scores). As the highest degradation was achieved for the most accurate matcher, this may suggest that the iris pattern ages to some extent. We hope this work attractively answers the call for iris ageing-related experiments, presently not numerous due to serious difficulties with collection of sufficiently lavish biometric ageing-related databases, and limited access to large number of iris matchers.},

  day          = {11-14},

  isbn         = {978-989-8565-36-5},

}

Ken Hughes and Kevin W. Bowyer, Hawaii International Conference on System Sciences (HICSS 46), January 7-10, 2013.

[pdf]

@INPROCEEDINGS{6480054, 

author={K. Hughes and K. W. Bowyer}, 

booktitle={2013 46th Hawaii International Conference on System Sciences}, 

title={Detection of Contact-Lens-Based Iris Biometric Spoofs Using Stereo Imaging}, 

year={2013}, 

volume={}, 

number={}, 

pages={1763-1772}, 

keywords={Cameras;Cornea;Image segmentation;Iris;Iris recognition;Lenses;Shape;biometrics;contact lenses;identify theft;iris recognition;spoof detection}, 

doi={10.1109/HICSS.2013.172}, 

ISSN={1530-1605}, 

month={Jan},}

J. Thompson, P. Flynn, K. Bowyer, H. Santos-Villalobos, Biometrics: Theory, Applications and Systems (BTAS), 2013.

[pdf]

@INPROCEEDINGS{6712693, 

author={J. Thompson and P. Flynn and K. Bowyer and H. Santos-Villalobos}, 

booktitle={2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)}, 

title={Effects of iris surface curvature on iris recognition}, 

year={2013}, 

volume={}, 

number={}, 

pages={1-8}, 

keywords={iris recognition;statistical analysis;iris recognition;iris surface curvature;matching ability;refractive power;statistical analysis;Imaging;Iris;Iris recognition;Lenses;Shape;Shape measurement;Splines (mathematics)}, 

doi={10.1109/BTAS.2013.6712693}, 

ISSN={}, 

month={Sept},

}

W. Vranderic, K. W. Bowyer, Biometrics: Theory, Applications and Systems (BTAS), 2013.

[pdf]

@INPROCEEDINGS{6712753, 

author={W. Vranderic and K. W. Bowyer}, 

booktitle={2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)}, 

title={Similarity of iris texture between siblings}, 

year={2013}, 

volume={}, 

number={}, 

pages={1-6}, 

keywords={image classification;image texture;iris recognition;automated texture analysis;iris image pair classification;iris texture similarity;sibling classification;Accuracy;Data acquisition;Educational institutions;Image segmentation;Iris;Iris recognition;Observers}, 

doi={10.1109/BTAS.2013.6712753}, 

ISSN={}, 

month={Sept},

}

J.S. Doyle, K.W. Bowyer, P.J. Flynn, Biometrics: Theory, Applications and Systems (BTAS), 2013.

[pdf]

@INPROCEEDINGS{6712745, 

author={J. S. Doyle and K. W. Bowyer and P. J. Flynn}, 

booktitle={2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)}, 

title={Variation in accuracy of textured contact lens detection based on sensor and lens pattern}, 

year={2013}, 

volume={}, 

number={}, 

pages={1-7}, 

keywords={image sensors;image texture;iris recognition;lenses;automatic detection;iris sensor;lens pattern;sensor pattern;textured contact lens detection;textured lens detection;Accuracy;Feature extraction;Iris;Iris recognition;Lenses;Training}, 

doi={10.1109/BTAS.2013.6712745}, 

ISSN={}, 

month={Sept},

}

A. Sgroi, K.W. Bowyer, P.J. Flynn, P.J. Phillips, Biometrics: Theory, Applications and Systems (BTAS), 2013.

[pdf]

@INPROCEEDINGS{6712697, 

author={A. Sgroi and K. W. Bowyer and P. Flynn and P. J. Phillips}, 

booktitle={2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)}, 

title={SNoW: Understanding the causes of strong, neutral, and weak face impostor pairs}, 

year={2013}, 

volume={}, 

number={}, 

pages={1-8}, 

keywords={face recognition;FRVT;SNoW;face recognition vendor test;impostor distribution;neutral partition;strong neutral;weak face impostor pairs;Algorithm design and analysis;Face;Face recognition;Image recognition;Partitioning algorithms;Probes;Snow}, 

doi={10.1109/BTAS.2013.6712697}, 

ISSN={}, 

month={Sept},

}

E. Ortiz, K.W. Bowyer, P.J. Flynn, Biometrics: Theory, Applications and Systems (BTAS), 2013.

[pdf]

K. McGinn, S. Tarin, K.W. Bowyer,  Biometrics: Theory, Applications and Systems (BTAS), 2013.

[pdf]

@inproceedings{mcginn2013identity,

  title={Identity verification using iris images: Performance of human examiners},

  author={McGinn, Kevin and Tarin, Samuel and Bowyer, Kevin W},

  booktitle={Biometrics: Theory, Applications and Systems (BTAS), 2013 IEEE Sixth International Conference on},

  pages={1--6},

  year={2013},

  organization={Citeseer}

}

J.Ross. Beveridge, P. Jonathon Phillips, David S. Bolme, Bruce A. Draper, Geof H. Given, Yui Man Lui, Mohammad Nayeem Teli,  Hao Zhang, W. Todd Scruggs, Kevin W. Bowyer, Patrick H. Flynn, Su Cheng, Biometrics: Theory, Applications and Systems (BTAS), 2013.

[pdf]

@INPROCEEDINGS{6712704, 

author={J. R. Beveridge and P. J. Phillips and D. S. Bolme and B. A. Draper and G. H. Givens and Y. M. Lui and M. N. Teli and H. Zhang and W. T. Scruggs and K. W. Bowyer and P. J. Flynn and S. Cheng}, 

booktitle={2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)}, 

title={The challenge of face recognition from digital point-and-shoot cameras}, 

year={2013}, 

volume={}, 

number={}, 

pages={1-8}, 

keywords={cameras;face recognition;social networking (online);PaSC;Point-And-Shoot Face Recognition Challenge;automatic recognition;commercial algorithm;digital point-and-shoot camera technology;error rates;face recognition technology;person recognition;public baseline algorithms;social network technology;still image to still image comparison;still image to video comparision;video to video comparison;Algorithm design and analysis;Cameras;Face;Face recognition;Protocols;Sensors;Videos}, 

doi={10.1109/BTAS.2013.6712704}, 

ISSN={}, 

month={Sept},

}

Sarah Baker, Kevin W. Bowyer, Patrick J. Flynn and P. Jonathon Phillips, in Handbook of Iris Recognition, Mark Burge and Kevin W. Bowyer, editors, Springer, 2013.

[pdf] [dataset]

Ryan Connaughton, Kevin W. Bowyer and Patrick Flynn, in Handbook of Iris Recognition, Mark Burge and Kevin W. Bowyer, editors, Springer, 2013.

[pdf]

@Inbook{Connaughton2013,

author="Connaughton, Ryan

and Bowyer, Kevin W.

and Flynn, Patrick J.",

editor="Burge, Mark J.

and Bowyer, Kevin W.",

title="Fusion of Face and Iris Biometrics",

bookTitle="Handbook of Iris Recognition",

year="2013",

publisher="Springer London",

address="London",

pages="219--237",

abstract="This chapter presents a system which simultaneously acquires face and iris samples using a single sensor, with the goal of improving recognition accuracy while minimizing sensor cost and acquisition time. The resulting system improves recognition rates beyond the observed recognition rates for either isolated biometrics.",

isbn="978-1-4471-4402-1",

doi="10.1007/978-1-4471-4402-1_12",

url="https://doi.org/10.1007/978-1-4471-4402-1_12"

}

Kevin W. Bowyer, Karen P. Hollingsworth and Patrick J. Flynn, in Handbook of Iris Recognition, Mark Burge and Kevin W. Bowyer, editors, Springer, 2013.

[pdf]

@Inbook{Bowyer2013,

author="Bowyer, Kevin W.

and Hollingsworth, Karen P.

and Flynn, Patrick J.",

editor="Burge, Mark J.

and Bowyer, Kevin W.",

title="A Survey of Iris Biometrics Research: 2008--2010",

bookTitle="Handbook of Iris Recognition",

year="2013",

publisher="Springer London",

address="London",

pages="15--54",

abstract="A recent survey of iris biometric research from its inception through 2007, roughly 15 years of research, lists approximately 180 publications. This new survey is intended to update the previous one, and covers iris biometrics research over the period of roughly 2008--2010. Research in iris biometrics has expanded so much that, although covering only 3 years and intentionally being selective about coverage, this new survey lists a larger number of references than the inception-through-2007 survey.",

isbn="978-1-4471-4402-1",

doi="10.1007/978-1-4471-4402-1_2",

url="https://doi.org/10.1007/978-1-4471-4402-1_2"

}

Sarah E Baker, Kevin W Bowyer, Patrick J Flynn, P Jonathon Phillips, Handbook of Iris Recognition, Spring, London, pp. 205-218, 2013.

[pdf]

@Inbook{Baker2013,

author="Baker, Sarah E.

and Bowyer, Kevin W.

and Flynn, Patrick J.

and Phillips, P. Jonathon",

editor="Burge, Mark J.

and Bowyer, Kevin W.",

title="Template Aging in Iris Biometrics",

bookTitle="Handbook of Iris Recognition",

year="2013",

publisher="Springer London",

address="London",

pages="205--218",

abstract="Using a data set with approximately 4 years of elapsed time between the earliest and most recent images of an iris (23 subjects, 46 irises, 6,797 images), we investigate template aging for iris biometrics. We compare the match and non-match distributions for short-time-lapse image pairs, acquired with no more than 120 days of time lapse between them, to the distributions for long-time-lapse image pairs, with at least 1,200 days of time lapse. We find no substantial difference in the non-match, or impostor, distribution between the short-time-lapse and the long-time-lapse data. We do find a difference in the match, or authentic, distributions. For the image data set and iris biometric systems used in this work, the false reject rate increases by about 50{\%} or greater for the long-time-lapse data relative to the short-time-lapse data. The magnitude of the increase in the false reject rate varies with changes in the decision threshold and with different matching algorithms. Our results demonstrate that iris biometrics is subject to a template aging effect.",

isbn="978-1-4471-4402-1",

doi="10.1007/978-1-4471-4402-1_11",

url="https://doi.org/10.1007/978-1-4471-4402-1_11"

}

P Jonathon Phillips, Patrick J Flynn, Handbook of Iris Recognition, Springer, London, pp. 85-102, 2013.

[pdf]

@Inbook{Phillips2013,

author="Phillips, P. Jonathon

and Flynn, Patrick J.",

editor="Burge, Mark J.

and Bowyer, Kevin W.",

title="Quality and Demographic Investigation of ICE 2006",

bookTitle="Handbook of Iris Recognition",

year="2013",

publisher="Springer London",

address="London",

pages="85--102",

abstract="There have been four major experimental evaluations of iris recognition technology in recent years: the ITIRT evaluation conducted by the International Biometric Group, the Iris 2006 evaluation conducted by Authenti-Corp, and the Iris Challenge Evaluation (ICE) 2006 and Iris Exchange (IREX) conducted by the National Institute of Standards and Technology. These experimental evaluations employed different vendor technologies and experimental specifications but yield consistent results in the areas where the specifications intersect. In the ICE 2006, participants were allowed to submit quality measures. We investigate the properties of their quality submissions.",

isbn="978-1-4471-4402-1",

doi="10.1007/978-1-4471-4402-1_5",

url="https://doi.org/10.1007/978-1-4471-4402-1_5"

}


2012

Feng Shen, Patrick J Flynn, Homeland Security (HST), 2012 IEEE Conference on Technologies for, IEEE, pp. 208-213, November 2012.

[pdf]

@INPROCEEDINGS{6459851, 

author={F. Shen and P. J. Flynn}, 

booktitle={2012 IEEE Conference on Technologies for Homeland Security (HST)}, 

title={Iris matching by crypts and anti-crypts}, 

year={2012}, 

volume={}, 

number={}, 

pages={208-213}, 

keywords={band-pass filters;image representation;image texture;iris recognition;anticrypts;band-pass filter;biometric technology;crypts;feature-based iris representation;forensics;identity recognition;identity verification;iris matching;iris recognition;iris texture;Bismuth;Cryptography;Feature extraction;Image edge detection;Iris;Iris recognition;Noise}, 

doi={10.1109/THS.2012.6459851}, 

ISSN={}, 

month={Nov},}

Soma Biswas, Kevin W. Bowyer and Patrick J. Flynn, IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (10), 2019-2030, October 2012.

[pdf]

@ARTICLE{6112780, 

author={S. Biswas and K. W. Bowyer and P. J. Flynn}, 

journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 

title={Multidimensional Scaling for Matching Low-Resolution Face Images}, 

year={2012}, 

volume={34}, 

number={10}, 

pages={2019-2030}, 

keywords={Cameras;Face recognition;Iterative methods;Probes;Spatial resolution;Face recognition;iterative majorization.;low-resolution matching;multidimensional scaling;Algorithms;Biometric Identification;Face;Humans;Image Processing, Computer-Assisted;Models, Statistical}, 

doi={10.1109/TPAMI.2011.278}, 

ISSN={0162-8828}, 

month={Oct},}

Nisha Srinivas, Gaurav Aggarwal, Patrick J Flynn, Richard W Vorder Bruegge, IEEE Transactions on Information Forensics and Security, IEEE, vol. 7, pp. 1536-1550, October 2012.

[pdf]

@ARTICLE{6228529, 

author={N. Srinivas and G. Aggarwal and P. J. Flynn and R. W. Vorder Bruegge}, 

journal={IEEE Transactions on Information Forensics and Security}, 

title={Analysis of Facial Marks to Distinguish Between Identical Twins}, 

year={2012}, 

volume={7}, 

number={5}, 

pages={1536-1550}, 

keywords={face recognition;gradient methods;humanities;mathematical operators;transforms;automatically detected facial marks;biometric signatures;bright region detection;dark region detection;face recognition systems;facial appearance;facial marks analysis;fast radial symmetry transform;gradient-based operator;identical twin face recognition;manually annotated facial marks;multiscale automatic facial mark detector;Detectors;Face;Face recognition;Image color analysis;Manuals;Observers;Skin;Face recognition;facial marks;identical twins}, 

doi={10.1109/TIFS.2012.2206027}, 

ISSN={1556-6013}, 

month={Oct},}

Hoang Bui, Peter Bui, Patrick Flynn, Douglas Thain, Distributed and Parallel Databases, Springer US, vol. 30, pp. 325-350, October 2012.

[pdf]

@Article{Bui2012,

author="Bui, Hoang

and Bui, Peter

and Flynn, Patrick

and Thain, Douglas",

title="ROARS: a robust object archival system for data intensive scientific computing",

journal="Distributed and Parallel Databases",

year="2012",

month="Oct",

day="01",

volume="30",

number="5",

pages="325--350",

abstract="As scientific research becomes more data intensive, there is an increasing need for scalable, reliable, and high performance storage systems. Such data repositories must provide both data archival services and rich metadata, and cleanly integrate with large scale computing resources. ROARS is a hybrid approach to distributed storage that provides both large, robust, scalable storage and efficient rich metadata queries for scientific applications. In this paper, we present the design and implementation of ROARS, focusing primarily on the challenge of maintaining data integrity across long time scales. We evaluate the performance of ROARS on a storage cluster, comparing to the Hadoop distributed file system and a centralized file server. We observe that ROARS has read and write performance that scales with the number of storage nodes, and integrity checking that scales with the size of the largest node. We demonstrate the ability of ROARS to function correctly through multiple system failures and reconfigurations. ROARS has been in production use for over three years as the primary data repository for a biometrics research lab at the University of Notre Dame.",

issn="1573-7578",

doi="10.1007/s10619-012-7103-5",

url="https://doi.org/10.1007/s10619-012-7103-5"

}

Robert Hasegawa, Estefan Ortiz, Kevin W. Bowyer, Louise Stark, Patrick J. Flynn and Ken Hughes, Biometrics Theory, Applications and Systems (BTAS) September 23-27, 2012.

[pdf]

@INPROCEEDINGS{6374598, 

author={R. Hasegawa and K. W. Bowyer and P. J. Flynn and E. Ortiz and L. Stark and K. Hughes}, 

booktitle={2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)}, 

title={Synthetic eye images for pupil dilation mitigation}, 

year={2012}, 

volume={}, 

number={}, 

pages={339-345}, 

keywords={image matching;iris recognition;probability;dilation values;enrolled eye image;enrollment samples;false nonmatch probability;iris images;probe eye image;pupil dilation mitigation;pupil size;real image;recognition accuracy;synthetic eye images;Biomedical imaging;Educational institutions;Image segmentation;Iris;Iris recognition;NIST;Probes}, 

doi={10.1109/BTAS.2012.6374598}, 

ISSN={}, 

month={Sept},}

Jeremiah Barr, Kevin W. Bowyer, Patrick Flynn, Soma Biswas, International Journal of Pattern Recognition and Artificial Intelligence 26 (5), August 2012.

[pdf]

@article{barr2012face,
  title={Face recognition from video: A review},
  author={Barr, Jeremiah R and Bowyer, Kevin W and Flynn, Patrick J and Biswas, Soma},
  journal={International Journal of Pattern Recognition and Artificial Intelligence},
  volume={26},
  number={05},
  pages={1266002},
  year={2012},
  publisher={World Scientific}
}

Jim Thomas, Ahsan Kareem and Kevin W. Bowyer, IEEE Interational Geoscience and Remote Sensing Symposium (IGARSS), July 2012.

[pdf]

@inproceedings{thomas2012fast,

  title={Fast, robust feature-based matching for automatic image registration in disaster response applications},

  author={Thomas, J and Kareem, A and Bowyer, KW},

  booktitle={IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},

  year={2012}

}

Adam Czajka, Krzysztof Piech, Journal of Telecommunications and Information Technology (JTIT), Vol. 3, str. 40-49, 2012.

[pdf]

@Article{Czajka_JTIT_2012,

  author   = {Adam Czajka and Krzysztof Piech},

  title    = {Secure Biometric Verification Station Based on Iris Recognition},

  journal  = {Journal of Telecommunications and Information Technology},

  year     = {2012},

  pages    = {40-49},

  abstract = {This paper describes an application of the Zak-Gabor-based iris coding to build a secure biometric verification station (SBS), consisting of a professional iris capture camera, a processing unit with specially designed iris recognition and communication software, as well as a display (LCD). Specially designed protocol controls the access to the station and secures the communication between the station and the external world. Reliability of the Zak-Gabor-based coding, similarly to other wavelet-based methods, strongly depends on appropriate choice of the wavelets employed in image coding. This choice cannot be arbitrary and should be adequate to the employed iris image quality. Thus in this paper we propose an automatic iris feature selection mechanism employing, among others, the minimum redundancy, maximum relevance (mRMR) methodology as one, yet important, step to assess the optimal set of wavelets used in this iris recognition application. System reliability is assessed with approximately 1000 iris images collected by the station for 50 different eyes.#url#},

  issue    = {3},

  keywords = {feature selection, application of biometrics, iris recognition; Zak-Gabor-based iris coding},

  url      = {https://www.itl.waw.pl/czasopisma/JTIT/2012/3/40.pdf},

  vol      = {2012},

}

Kevin W. Bowyer, Pattern Recognition Letters 33 (8), 965-969, June 2012.

[pdf]

@article{BOWYER2012965,

title = "The results of the NICE.II Iris biometrics competition",

journal = "Pattern Recognition Letters",

volume = "33",

number = "8",

pages = "965 - 969",

year = "2012",

note = "Noisy Iris Challenge Evaluation II - Recognition of Visible Wavelength Iris Images Captured At-a-distance and On-the-move",

issn = "0167-8655",

doi = "https://doi.org/10.1016/j.patrec.2011.11.024",

url = "http://www.sciencedirect.com/science/article/pii/S0167865511004144",

author = "Kevin W. Bowyer",

keywords = "Biometrics, Iris biometrics, Performance evaluation"

}

Ryan Connaughton, Amanda Sgroi, Kevin W. Bowyer and Patrick J. Flynn, IEEE Transactions on Information Forensics and Security 7 (3), 919-931, June 2012.

[pdf]

@ARTICLE{6168257, 

author={R. Connaughton and A. Sgroi and K. Bowyer and P. J. Flynn}, 

journal={IEEE Transactions on Information Forensics and Security}, 

title={A Multialgorithm Analysis of Three Iris Biometric Sensors}, 

year={2012}, 

volume={7}, 

number={3}, 

pages={919-931}, 

keywords={image matching;iris recognition;open systems;sensors;cross-sensor matching;cross-sensor performance;dilation ratio;interoperability;iris biometric sensor;iris biometric system;iris matching system;multialgorithm analysis;single-sensor performance;Biosensors;Cameras;Context;Iris recognition;Lighting;Software;Biometrics;interoperability;iris recognition;sensor evaluation}, 

doi={10.1109/TIFS.2012.2190575}, 

ISSN={1556-6013}, 

month={June},}

Amanda Sgroi, Kevin W. Bowyer and Patrick Flynn, IEEE Computer Society Biometrics Workshop, June 2012.

[pdf]

@INPROCEEDINGS{6239215, 

author={A. Sgroi and K. W. Bowyer and P. Flynn}, 

booktitle={2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, 

title={Effects of dominance and laterality on iris recognition}, 

year={2012}, 

volume={}, 

number={}, 

pages={52-58}, 

keywords={iris recognition;eye dominance;eyedness;handedness;iris biometrics;iris recognition;single-eye recognition system;subject population;Accuracy;Bars;Correlation;Iris recognition;Psychology;Sensors}, 

doi={10.1109/CVPRW.2012.6239215}, 

ISSN={2160-7508}, 

month={June},}

Samuel P. Fenker and Kevin W. Bowyer, IEEE Computer Society Biometrics Workshop, June 2012.

[pdf]

@INPROCEEDINGS{6239214, 

author={S. P. Fenker and K. W. Bowyer}, 

booktitle={2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, 

title={Analysis of template aging in iris biometrics}, 

year={2012}, 

volume={}, 

number={}, 

pages={45-51}, 

keywords={image matching;iris recognition;biometric template aging analysis;enrollment image;iris biometrics;iris image pairs;iris matcher;match score distribution;verification image;Aging;Humans;Iris recognition;Lighting;Magnetic resonance;Springs}, 

doi={10.1109/CVPRW.2012.6239214}, 

ISSN={2160-7508}, 

month={June},}

Karen P. Hollingsworth, Shelby S. Darnell, Philip E. Miller, Damon L. Woodard, Kevin W. Bowyer and Patrick J. Flynn, IEEE Transactions on Information Forensics and Security 7 (2), 588-601, April 2012.

[pdf]

@ARTICLE{6062410, 

author={K. P. Hollingsworth and S. S. Darnell and P. E. Miller and D. L. Woodard and K. W. Bowyer and P. J. Flynn}, 

journal={IEEE Transactions on Information Forensics and Security}, 

title={Human and Machine Performance on Periocular Biometrics Under Near-Infrared Light and Visible Light}, 

year={2012}, 

volume={7}, 

number={2}, 

pages={588-601}, 

keywords={infrared sources;iris recognition;lighting;performance evaluation;computer algorithms;eye;face biometrics;human performance;illuminations;iris biometrics;machine performance;near infrared light;periocular biometrics;untrained participants;visible light;Accuracy;Face;Face recognition;Humans;Iris recognition;Lighting;Iris recognition;near-infrared (NIR) light;ocular biometrics;periocular recognition;visible light (VL)}, 

doi={10.1109/TIFS.2011.2173932}, 

ISSN={1556-6013}, 

month={April},}

Rainer Stiefelhagen, Marian Stewart Bartlett and Kevin W. Bowyer, Image and Vision Computing Journal 30 (3), 135, March 2012. 

[link]

Karen Hollingsworth, Kevin W. Bowyer, Patrick J. Flynn, Journal of Intelligence Community Research and Development, permanently available on Intelink, 2012.

[pdf]

@article{HOLLINGSWORTH2011707,

title = "Useful features for human verification in near-infrared periocular images",

journal = "Image and Vision Computing",

volume = "29",

number = "11",

pages = "707 - 715",

year = "2011",

issn = "0262-8856",

doi = "https://doi.org/10.1016/j.imavis.2011.09.002",

url = "http://www.sciencedirect.com/science/article/pii/S0262885611000953",

author = "Karen Hollingsworth and Kevin W. Bowyer and Patrick J. Flynn",

keywords = "Periocular recognition, Ocular biometrics, Near-infrared light"

}

Jim Thomas, Kevin W. Bowyer, Ahsan Kareem, IEEE Workshop on Applications of Computer Vision January 2012, Colorado Springs, Colorado, USA.

[pdf]

@INPROCEEDINGS{6163047, 

author={J. Thomas and K. W. Bowyer and A. Kareem}, 

booktitle={2012 IEEE Workshop on the Applications of Computer Vision (WACV)}, 

title={Color balancing for change detection in multitemporal images}, 

year={2012}, 

volume={}, 

number={}, 

pages={385-390}, 

keywords={cameras;computational geometry;image colour analysis;object detection;automatic color balancing approaches;camera positions;change detection;color balancing approach;color correction approach;color palette;global color characteristics;lighting content;multitemporal images;scene geometries;structural content;Computer vision;Databases;Image color analysis;Image segmentation;Integral equations;Lighting;Mathematical model}, 

doi={10.1109/WACV.2012.6163047}, 

ISSN={1550-5790}, 

month={Jan},

}

D. Thain, P. Bui, M. Albrecht, R. Charmichael, H. Bui, S. Emrich, P. Flynn, Data Intensive Distributed Computing (T. Kosar, ed.), IGI Global (Information Science Reference imprint), 2012.

[link]

@incollection{thain2012data,

  title={Data intensive computing with clustered chirp servers},

  author={Thain, Douglas and Albrecht, Michael and Bui, Hoang and Bui, Peter and Carmichael, Rory and Emrich, Scott and Flynn, Patrick},

  booktitle={Data Intensive Distributed Computing: Challenges and Solutions for Large-scale Information Management},

  pages={140--154},

  year={2012},

  publisher={IGI Global}

}

Gaurav Aggarwal, Soma Biswas, Patrick Flynn, Kevin Bowyer, Proc. IEEE Workshop on Applications of Computer Vision (WACV 2012), January 2012, Breckenridge, Colorado, USA.

[pdf]

@INPROCEEDINGS{6163008, 

author={G. Aggarwal and S. Biswas and P. J. Flynn and K. W. Bowyer}, 

booktitle={2012 IEEE Workshop on the Applications of Computer Vision (WACV)}, 

title={A sparse representation approach to face matching across plastic surgery}, 

year={2012}, 

volume={}, 

number={}, 

pages={113-119}, 

keywords={face recognition;image matching;surgery;face matching algorithms;facial appearance;local facial characteristics;part-wise facial characterization;plastic surgery database;plastic surgery procedures;sequestered nongallery subjects;sparse representation approach;Databases;Face;Face recognition;Facial features;Plastics;Surgery;Training}, 

doi={10.1109/WACV.2012.6163008}, 

ISSN={1550-5790}, 

month={Jan},

}

Gaurav Aggarwal, Soma Biswas, Patrick Flynn, Kevin Bowyer, Proc. IEEE Workshop on Application of Computer Vision (WACV 2012), January 2012, Breckenridge, Colorado, USA.

[pdf]

@INPROCEEDINGS{6163007, 

author={G. Aggarwal and S. Biswas and P. J. Flynn and K. W. Bowyer}, 

booktitle={2012 IEEE Workshop on the Applications of Computer Vision (WACV)}, 

title={Predicting good, bad and ugly match Pairs}, 

year={2012}, 

volume={}, 

number={}, 

pages={153-160}, 

keywords={face recognition;image matching;least squares approximations;regression analysis;GBU challenge problem;face matching performance;facial appearance variation;facial characteristics;hue;image characteristics;image sharpness;match pairs;partial least squares based regression;saturation;Algorithm design and analysis;Face;Face recognition;Image edge detection;Measurement;Partitioning algorithms;Vectors}, 

doi={10.1109/WACV.2012.6163007}, 

ISSN={1550-5790}, 

month={Jan},

}

Patrick J Flynn, 2012.

[pdf]

@article{flynn2012science,

  title={Science Panel Discussion presentation:" You Want to Do What? Managing and Distributing Identifying Data without Running Afoul of Your Research Sponsor, Your IRB, or Your Office of Counsel"},

  author={Flynn, Patrick J},

  year={2012}

}

Anmar Abuhamdah, Rawnaq Kittaneh, Jawad Alkhatib, Mohd Zakree Ahmad Nazri, A Abuhamdah, A Abuhamdah, BM El-Zaghmouri, A Quteishat, R Kittaneh, MJ Berry, GS Linoff, P Brucker, S Dasgupta, Y Freund, I Davidson, A Satyanarayana, M Halkidi, Y Batistakis, M Vazirgiannis, J Holland, S Hong, AK Jain, MN Murty, PJ Flynn, M Mahajan, P Nimbhorkar, K Varadarajan, I Saha, D Pewczynski, U Maulik, S Bandyopadhyay, X Wang, Y Liu, Z Yi, H Wu, M Ye, K Chen, C Zhang, D Ouyang, J Ning, Journal of Applied Sciences, orgz, vol. 14, pp 345-360, 2012.

[pdf]

@article{ZHANG20104761,

title = "An artificial bee colony approach for clustering",

journal = "Expert Systems with Applications",

volume = "37",

number = "7",

pages = "4761 - 4767",

year = "2010",

issn = "0957-4174",

doi = "https://doi.org/10.1016/j.eswa.2009.11.003",

url = "http://www.sciencedirect.com/science/article/pii/S0957417409009452",

author = "Changsheng Zhang and Dantong Ouyang and Jiaxu Ning",

keywords = "Clustering, Meta-heuristic algorithm, Artificial bee colony, -means"

}

Iman Mohammed Burhan, Md Jan Nordin, BK Alese, SA Mogaji, OS Adewale, O Daramola, C BenAbdelkader, R Cutler, L Davis, L Bianchi, D Angelini, F Lacquaniti, AF Bobick, AY Johnson, NV Boulgouris, ZX Chi, EJ Candes, X Li, Y Ma, J Wright, C Canton-Ferrer, JR Casas, M Pardas, Y Chen, Q Wu, X He, MR Dawson, JP Foster, MS Nixon, A Prugel-Bennett, H Lu, KN Plataniotis, AN Venetsanopoulos, K Holien, AK Jain, P Flynn, AA Ross, W Kusakunniran, Q Wu, H Li, J Zhang, CP Lee, AW Tan, SC Tan, BB Mjaaland, C Murukesh, DK Thanushkodi, H Ng, WH Tan, J Abdullah, HL Tong, MS Nixon, T Tan, R Chellappa, PJ Phillips, HJ Moon, SA Rizvi, PJ Rauss, R Rosipal, N Kramer, L Wang, T Tan, H Ning, W Hu, L Wang, X Geng, Y Wang, K Huang, T Tan, S Yu, D Tan, T Tan, S Zheng, J Zhang, K Huang, R He, T Tan, Asian Journal of Applied Sciences, orgz, vol. 8 , pp. 1102-1110, 2012.

[pdf]

@INPROCEEDINGS{6115889, 

author={S. Zheng and J. Zhang and K. Huang and R. He and T. Tan}, 

booktitle={2011 18th IEEE International Conference on Image Processing}, 

title={Robust view transformation model for gait recognition}, 

year={2011}, 

volume={}, 

number={}, 

pages={2073-2076}, 

keywords={correlation methods;feature extraction;gait analysis;image recognition;least squares approximations;principal component analysis;CASIA gait dataset;carrying condition changes;clothing condition changes;feature selection method;gait energy image;gait recognition systems;large intra-class variations;partial least square;robust principal component analysis;robust view transformation model;shared linear correlated low rank subspace;viewing angle variation;Conferences;Feature extraction;Image recognition;Legged locomotion;Probes;Robustness;Vectors;Gait Recognition;Low-rank;View Transformation Model}, 

doi={10.1109/ICIP.2011.6115889}, 

ISSN={1522-4880}, 

month={Sept},}

2011

Karen Hollingsworth, Kevin W. Bowyer and Patrick J. Flynn, IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (12), 2465-2476, December 2011.

[pdf]

@ARTICLE{,

author = {P. J. Flynn and K. W. Bowyer and K. P. Hollingsworth},

journal = {IEEE Transactions on Pattern Analysis & Machine Intelligence},

title = {Improved Iris Recognition through Fusion of Hamming Distance and Fragile Bit Distance},

year = {2011},

volume = {33},

number = {},

pages = {2465-2476},

keywords={Iris recognition;Hamming distance;Biometrics;Cameras;score fusion.;Iris biometrics;fragile bits},

doi = {10.1109/TPAMI.2011.89},

url = {doi.ieeecomputersociety.org/10.1109/TPAMI.2011.89},

ISSN = {0162-8828},

month={12}

}

Vipin Vijayan, Kevin Bowyer, Patrick Flynn, Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, IEEE, pp. 2100-2105, November 2011.

[pdf]

@INPROCEEDINGS{6130507, 
author={V. Vijayan and K. Bowyer and P. Flynn}, 
booktitle={2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)}, 
title={3D Twins and Expression Challenge}, 
year={2011}, 
volume={}, 
number={}, 
pages={2100-2105}, 
keywords={face recognition;visual databases;3D face recognition algorithm;3D scans;3D twins expression challenge;FRGC v2 dataset;facial expressions;neutral expression;smiling expression;Face;Face recognition;Humans;Iterative closest point algorithm;Lighting;Probes;Three dimensional displays}, 
doi={10.1109/ICCVW.2011.6130507}, 
ISSN={}, 
month={Nov},}

Stephen Lagree, Kevin W. Bowyer, IEEE International Conference on Technologies for Homeland Security, November 2011, Boston, Massachusetts, USA.

[pdf]

@INPROCEEDINGS{6107909, 
author={S. Lagree and K. W. Bowyer}, 
booktitle={2011 IEEE International Conference on Technologies for Homeland Security (HST)}, 
title={Predicting ethnicity and gender from iris texture}, 
year={2011}, 
volume={}, 
number={}, 
pages={440-445}, 
keywords={image texture;iris recognition;ethnicity prediction;gender prediction;iris texture;Accuracy;Decision trees;Detectors;Iris;Iris recognition;Training;Training data;ethnicity prediction;gender prediction;iris biometric;soft biometric;texture analysis}, 
doi={10.1109/THS.2011.6107909}, 
ISSN={}, 
month={Nov},

}

Soma Biswas, Gaurav Aggarwal, Kevin Bowyer, Patrick Flynn, aProc.  2011 IEEE International Workshop on Information Forensics and Security (WIFS 2011), November-December 2011, Foz do Iguacu, Brazil.

[pdf]

@INPROCEEDINGS{6123126, 
author={S. Biswas and K. W. Bowyer and P. J. Flynn}, 
booktitle={2011 IEEE International Workshop on Information Forensics and Security}, 
title={A study of face recognition of identical twins by humans}, 
year={2011}, 
volume={}, 
number={}, 
pages={1-6}, 
keywords={face recognition;image matching;automatic face recognition algorithms;automatic face recognition systems;facial images;human face matching capability;human observers;identical human twins;Biomedical imaging;Humans;Image recognition}, 
doi={10.1109/WIFS.2011.6123126}, 
ISSN={2157-4766}, 
month={Nov},

}

Jeffrey Paone, P. Flynn, Proc. 2011 IEEE International Workshop on Information Forensics and Security (WIFS 2011), November-December 2011, Foz do Iquacu, Brazil.

Silver Best Student Paper Award at WIFS 2011

[pdf]

@INPROCEEDINGS{6123158, 
author={J. Paone and P. J. Flynn}, 
booktitle={2011 IEEE International Workshop on Information Forensics and Security}, 
title={On the consistency of the biometric menagerie for irises and iris matchers}, 
year={2011}, 
volume={}, 
number={}, 
pages={1-6}, 
keywords={image classification;image matching;iris recognition;biometric menagerie classifications;biometric recognition system;iris matchers;iris recognition;Animals;Grippers;Measurement;Phantoms}, 
doi={10.1109/WIFS.2011.6123158}, 
ISSN={2157-4766}, 
month={Nov},

}

Karen Hollingsworth, Kevin W. Bowyer, Stephen Lagree, Samuel P. Fenker and Patrick J. Flynn, Computer Vision and Image Understanding 115, 1493-1502, 2011.

[pdf]

@article{HOLLINGSWORTH20111493,

title = "Genetically identical irises have texture similarity that is not detected by iris biometrics",

journal = "Computer Vision and Image Understanding",

volume = "115",

number = "11",

pages = "1493 - 1502",

year = "2011",

issn = "1077-3142",

doi = "https://doi.org/10.1016/j.cviu.2011.06.010",

url = "http://www.sciencedirect.com/science/article/pii/S107731421100155X",

author = "Karen Hollingsworth and Kevin W. Bowyer and Stephen Lagree and Samuel P. Fenker and Patrick J. Flynn",

keywords = "Ocular biometrics, Iris recognition, Periocular recognition, Monozygotic twins, Genetically identical irises, Texture analysis"

}

Vipin Vijayan, Kevin Bowyer, Patrick Flynn, Di Huang, Liming Chen, Mark Hansen, Shishir Shah, Omar Ocegueda, Ioannis Kakadiaris, Proc. 2011 IEEE International Joint Conference on Biometrics (IJCB 2011), October 2011, Washington, DC, USA.

[pdf]

@INPROCEEDINGS{6117491, 
author={V. Vijayan and K. W. Bowyer and P. J. Flynn and D. Huang and L. Chen and M. Hansen and O. Ocegueda and S. K. Shah and I. A. Kakadiaris}, 
booktitle={2011 International Joint Conference on Biometrics (IJCB)}, 
title={Twins 3D face recognition challenge}, 
year={2011}, 
volume={}, 
number={}, 
pages={1-7}, 
keywords={face recognition;3D TEC dataset;FRGC v2;neutral expression;public datasets;smiling expression;twins 3D face recognition challenge;Face;Probes}, 
doi={10.1109/IJCB.2011.6117491}, 
ISSN={}, 
month={Oct},

}

Matther Pruitt, Jason Grant, Jeffrey Paone, Patrick Flynn, Richard Vorder Bruegge, Proc. 2011 IEEE International Joint Conference on Biometrics (IJCB 2011), October 2011, Washington, DC, USA.

[pdf]

@INPROCEEDINGS{6117476, 
author={M. T. Pruitt and J. M. Grant and J. R. Paone and P. J. Flynn and R. W. V. Bruegge}, 
booktitle={2011 International Joint Conference on Biometrics (IJCB)}, 
title={Facial recognition of identical twins}, 
year={2011}, 
volume={}, 
number={}, 
pages={1-8}, 
keywords={biometrics (access control);face recognition;image matching;principal component analysis;Cognitec 8.3.2.0;PittPatt 4.2.1;VeriLook 4.0;baseline matcher;biometric identification systems;commercial face matchers;facial recognition;identical twins;local region PCA;Cameras;Glass;Probes}, 
doi={10.1109/IJCB.2011.6117476}, 
ISSN={}, 
month={Oct},

}

Jeffrey Paone, Soma Biswas, Gaurav Aggarwal, Patrick Flynn, Proc. 2011 IEEE International Joint Conference on Biometrics (IJCB 2011), October 2011, Washington, DC, USA.

[pdf]

@INPROCEEDINGS{6117551, 
author={J. Paone and S. Biswas and G. Aggarwal and P. Flynn}, 
booktitle={2011 International Joint Conference on Biometrics (IJCB)}, 
title={Difficult imaging covariates or difficult subjects? - An empirical investigation}, 
year={2011}, 
volume={}, 
number={}, 
pages={1-8}, 
keywords={biometrics (access control);face recognition;image classification;biometric menagerie-based classification;deployment sites;face recognition algorithms;identifying factors;imaging covariates;internal subject characterization;neutral expression image;smiling target image;Grippers;Neodymium;Phantoms}, 
doi={10.1109/IJCB.2011.6117551}, 
ISSN={}, 
month={Oct},

}

Soma Biswas, Guarav Aggarwal, Patrick Flynn, Proc. 2011 IEEE International Joint Conference on Biometrics (IJCB 2011), October 2011, Washington, DC, USA.

[pdf]

@INPROCEEDINGS{6117514, 
author={S. Biswas and G. Aggarwal and P. J. Flynn}, 
booktitle={2011 International Joint Conference on Biometrics (IJCB)}, 
title={Face recognition in low-resolution videos using learning-based likelihood measurement model}, 
year={2011}, 
volume={}, 
number={}, 
pages={1-7}, 
keywords={face recognition;image resolution;learning (artificial intelligence);video surveillance;face recognition algorithm;face tracking algorithm;frontal image quality;high-resolution gallery image matching;interEuclidean distances;learning-based likelihood measurement model;low resolution surveillance videos;real surveillance quality videos;uncontrolled pose;Computational modeling;Information filters;Laplace equations;Videos}, 
doi={10.1109/IJCB.2011.6117514}, 
ISSN={}, 
month={Oct},

}

Estefan Ortiz, Kevin W. Bowyer, International Joint Conference on Biometrics (IJCB 2011), October 2011.

[pdf]

@INPROCEEDINGS{6117526, 
author={E. Ortiz and K. W. Bowyer}, 
booktitle={2011 International Joint Conference on Biometrics (IJCB)}, 
title={Dilation aware multi-image enrollment for iris biometrics}, 
year={2011}, 
volume={}, 
number={}, 
pages={1-7}, 
keywords={eye;iris recognition;statistical distributions;best eye image technique;dilation aware multiimage enrollment;empirical dilation ratio distribution;false nonmatch probability;image acquisition;iris biometric systems;probe eye image;pupil dilation;Biological system modeling;Iris;Iris recognition;NIST}, 
doi={10.1109/IJCB.2011.6117526}, 
ISSN={}, 
month={Oct},

}

James Thomas, Ahsan Kareem, Kevin Bowyer, 13th International Conference on Wind Engineering (ICWE 13), July 2011.

[pdf]

@inproceedings{thomas2011towards,

  title={Towards a robust, automated hurricane damage assessment from high-resolution images},

  author={Thomas, Jim and Bowyer, KW and Kareem, A},

  booktitle={13th International Conference on Wind Engineering},

  pages={10--15},

  year={2011}

}

Kevin W. Bowyer, IEEE Computer 44 (7), pp. 100-102, July 2011.

[pdf]

@ARTICLE{5958714, 

author={K. W. Bowyer}, 

journal={Computer}, 

title={What Surprises Do Identical Twins Have for Identity Science?}, 

year={2011}, 

volume={44}, 

number={7}, 

pages={100-102}, 

keywords={face recognition;iris recognition;biometric dataset;face recognition;identical twins;identity science;iris recognition;Biometrics;Face recognition;Human factors;Iris recognition;Research and development;Three dimensional displays;Biometrics;Face recognition;Identity sciences;Iris recognition;Twins studies}, 

doi={10.1109/MC.2011.221}, 

ISSN={0018-9162}, 

month={July},}

A. Zavodny, P. Flynn, X. Chen, Proc. 2011 IEEE International Conference on Multimedia and Expo (ICME 2011) Workshop on Hot Topics in 3D Multimedia (Hot 3D), pp. 1 - 6, July 2011.

[pdf]

@INPROCEEDINGS{6012230, 
author={A. Zavodny and P. J. Flynn and Xin Chen}, 
booktitle={2011 IEEE International Conference on Multimedia and Expo}, 
title={Textured mesh generation of extracted regions from urban range-scanned LIDAR data}, 
year={2011}, 
volume={}, 
number={}, 
pages={1-6}, 
keywords={cameras;data acquisition;optical radar;optical scanners;archaeological preservation;camera imagery;city planning;data acquisition;disconnected points;extracted regions;inter-point relationships;logical surfaces;scan vehicle;textured mesh generation;urban modeling;urban range-scanned lidar data;Cameras;Image color analysis;Laser radar;Shape;Solid modeling;Solids;Three dimensional displays}, 
doi={10.1109/ICME.2011.6012230}, 
ISSN={1945-7871}, 
month={July},

}

R. Connaughton, A. Sgroi, K. Bowyer, P. Flynn, Proc. CVPR Workshop on Biometrics, pp. 90 - 97, June 2011.

[pdf]

@INPROCEEDINGS{5981814, 

author={R. Connaughton and A. Sgroi and K. W. Bowyer and P. Flynn}, 

booktitle={CVPR 2011 WORKSHOPS}, 

title={A cross-sensor evaluation of three commercial iris cameras for iris biometrics}, 

year={2011}, 

volume={}, 

number={}, 

pages={90-97}, 

keywords={image matching;image sensors;iris recognition;commercial iris cameras;cross-sensor evaluation;cross-sensor matching;iris biometrics;iris matching algorithms;iris sensors;Image sensors;Iris;Iris recognition;Prediction algorithms;Sensor systems}, 

doi={10.1109/CVPRW.2011.5981814}, 

ISSN={2160-7508}, 

month={June},

}

G. Aggarwal, S. Biswas, K. Bowyer, P. Flynn, Proc. CVPR Workshop on Biometrics, pp. 52-59, June 2011.

[pdf]

@INPROCEEDINGS{5981784, 

author={G. Aggarwal and S. Biswas and P. J. Flynn and K. W. Bowyer}, 

booktitle={CVPR 2011 WORKSHOPS}, 

title={Predicting performance of face recognition systems: An image characterization approach}, 

year={2011}, 

volume={}, 

number={}, 

pages={52-59}, 

keywords={face recognition;image matching;face matching systems;face recognition systems;image characterization approach;multi-PIE data;multidimensional scaling;novel imaging conditions;space characterizing imaging conditions;Face;Face recognition;Imaging;Lighting;Prediction algorithms;Training;Training data}, 

doi={10.1109/CVPRW.2011.5981784}, 

ISSN={2160-7508}, 

month={June},

}

N. Srinivas, G. Aggarwal, P, Flynn, R. Vorder Bruegge, Proc. CVPR Workshop on Biometrics, pp. 106 - 113, June 2011.

[pdf]

@INPROCEEDINGS{5981818, 

author={N. Srinivas and G. Aggarwal and P. J. Flynn and R. W. V. Bruegge}, 

booktitle={CVPR 2011 WORKSHOPS}, 

title={Facial marks as biometric signatures to distinguish between identical twins}, 

year={2011}, 

volume={}, 

number={}, 

pages={106-113}, 

keywords={image matching;observers;biometric signatures;face matching systems;facial mark annotataion;identical twins;observers;Bipartite graph;Face;Image color analysis;Iris recognition;Observers;Shape;Skin}, 

doi={10.1109/CVPRW.2011.5981818}, 

ISSN={2160-7508}, 

month={June},

}

Ryan Connaughton, Amanda Sgroi, Kevin W. Bowyer and Patrick J. Flynn, IEEE Computer Society Workshop on Biometrics, June 2011.
[pdf]

@INPROCEEDINGS{5981814, 

author={R. Connaughton and A. Sgroi and K. W. Bowyer and P. Flynn}, 

booktitle={CVPR 2011 WORKSHOPS}, 

title={A cross-sensor evaluation of three commercial iris cameras for iris biometrics}, 

year={2011}, 

volume={}, 

number={}, 

pages={90-97}, 

keywords={image matching;image sensors;iris recognition;commercial iris cameras;cross-sensor evaluation;cross-sensor matching;iris biometrics;iris matching algorithms;iris sensors;Image sensors;Iris;Iris recognition;Prediction algorithms;Sensor systems}, 

doi={10.1109/CVPRW.2011.5981814}, 

ISSN={2160-7508}, 

month={June},}

R. Connaughton, K. Bowyer, P. Flynn, Proc. 22nd Midwest Artificial Intelligence and Cognitive Science Conference (MAICS 2011), Cincinnati, pp. 99-106, April 2011.

[pdf]

@inproceedings{connaughton2011fusion,

  title={Fusion of Face and Iris Biometrics from a Stand-Off Video Sensor.},

  author={Connaughton, Ryan and Bowyer, Kevin W and Flynn, Patrick J},

  booktitle={MAICS},

  pages={99--106},

  year={2011}

}

J.S. Doyle Jr., P.J. Flynn, Proc. 22nd Midwest Artificial Intelligence and Cognitive Science Conference (MAICS 2011), Cincinnati, pp. 91 - 98, April 2011.

[pdf]

@inproceedings{doyle2011iris,

  title={Iris Quality in an Operational Context.},

  author={Doyle Jr, James S and Flynn, Patrick J},

  booktitle={MAICS},

  pages={91--98},

  year={2011}

}

Sarah E Baker, Patrick J Flynn, Kevin W Bowyer, PJ Phillips, NIST Interagency/Internal Report (NISTIR)-7752, March 2011.

[pdf]

@techreport{baker2011empirical,

  title={Empirical Evidence for Increased False Reject Rate with Time Lapse in ICE 2006},

  author={Baker, Sarah E and Flynn, Patrick J and Bowyer, Kevin W and Phillips, PJ},

  year={2011}

}

KKF Wong, Murugiah P Souppaya, Karen Scarfone, Paul Hoffman, Gillian Nave, Craig J Sansonetti, Csilla Szabo, John J Curry, Darren Smillie, Charles D Fenimore, Samuel Armato, Denise Aberle, Matthew Brown, Claudia Henschke, Michael McNitt-Gray, Heber MacMahon, Geoffrey McLennan, Charles R Meyer, Anthony P Reeves, David F Yankelevitz, Robert R Greenberg, Bode Peter, Fernandes Elisabete, Chiara F Ferraris, Haleh Azari, Yaw S Obeng, Kathleen C Richardson, Kuldeep R Prasad, William M Pitts, Jiann C Yang, David J Wineland, Dietrich G Leibfried, Alexander V Skripov, Terrence J Udovic, John J Rush, MA Uimin, Jon T Hougen, Nicole M Moore, Nancy J Lin, Nathan D Gallant, Matthew L Becker, Elisabeth Mansfield, Stephanie A Hooker, Mark A Kedzierski, Jeffrey W Bullard, Edward J Garboczi, William L George, Nicos Martys, Steven G Satterfield, Judith E Terrill, Julian S Taurozzi, Vincent A Hackley, Andrew D Ludlow, Yanyi Jiang, Nathan D Lemke, Richard W Fox, Jeffrey A Sherman, Long-Sheng Ma, Christopher W Oates, Ryan G Brennan, Melissa M Phillips, Liang Yueh Ou Yang, Thomas P Moffat, Yooyoung Lee, Ross J Micheals, James J Filliben, PJ Phillips, Kevin W Bowyer, Patrick J Flynn, Sarah E Baker, Uwe Arp, Ping-Shine Shaw, Zhigang Li, Alexander Gottwald, Mathias Richter, The Proceedings of the Twelfth East Asia-Pacific Conference on Structural Engineering and Construction-EASEC12, vol. 14, pp. 1645-1652, January 2011.

[pdf]

@inproceedings{wong2011seismic,

  title={Seismic applications of nonlinear response spectra based on the theory of modal analysis},

  author={Wong, KKF and Souppaya, Murugiah P and Scarfone, Karen and Hoffman, Paul and Nave, Gillian and Sansonetti, Craig J and Szabo, Csilla and Curry, John J and Smillie, Darren and Fenimore, Charles D and others},

  booktitle={The Proceedings of the Twelfth East Asia-Pacific Conference on Structural Engineering and Construction-EASEC12},

  volume={14},

  pages={1645--1652},

  year={2011}

}

P J Phillips, Kevin W Bowyer, Patrick J Flynn, Sarah E Baker, IEEE Transactions on Pattern Analysis and Machine Intelligence, January 2011.

[pdf]

@techreport{baker2011empirical,

  title={Empirical Evidence for Increased False Reject Rate with Time Lapse in ICE 2006},

  author={Baker, Sarah E and Flynn, Patrick J and Bowyer, Kevin W and Phillips, PJ},

  year={2011}

}

N. Srinivas, M. Pruitt, G. Aggarwal, P. Flynn, R. Vorder Bruegge,  abstracted in Proc. 63rd AAFS Annual Meeting (American Academy of Forensic Sciences), p. 140, January 2011, Chicago, Illinois, USA.

[slides]

@article{flynndocument,

  title={Document Title: Face Annotation at the Macro-scale and the Micro-scale: Tools, Techniques, and Applications in Forensic Identification},

  author={Flynn, Patrick J and Jain, Anil K}

}

Sam Fenker, Kevin W. Bowyer, Applications of Computer Vision, pp. 232-239, January 2011.

[pdf]

@INPROCEEDINGS{5711508, 
author={S. P. Fenker and K. W. Bowyer}, 
booktitle={2011 IEEE Workshop on Applications of Computer Vision (WACV)}, 
title={Experimental evidence of a template aging effect in iris biometrics}, 
year={2011}, 
volume={}, 
number={}, 
pages={232-239}, 
keywords={iris recognition;LG 2200;contact lenses;false rejection rate;iris biometric systems;iris imaging system;iris recognition algorithms;match hamming distance;pupil dilation;template aging effect;time 2 year;Aging;Hamming distance;High definition video;Iris recognition;Lenses;Software}, 
doi={10.1109/WACV.2011.5711508}, 
ISSN={1550-5790}, 
month={Jan},

}

Jeremiah Barr, Kevin W. Bowyer, Patrick J. Flynn, Applications of Computer Vision (WACV), 2011 IEEE Workshop on Applications of Computer Vision, pp. 182-189, January 2011.

[pdf]

@INPROCEEDINGS{5711501, 

author={J. R. Barr and K. W. Bowyer and P. J. Flynn}, 

booktitle={2011 IEEE Workshop on Applications of Computer Vision (WACV)}, 

title={Detecting questionable observers using face track clustering}, 

year={2011}, 

volume={}, 

number={}, 

pages={182-189}, 

keywords={face recognition;image sequences;object detection;pattern clustering;Neurotechnology;VeriLook face recognition software;detection rate;face image sequences;face track clustering;facial expression;false detection frequency;intermittent occlusions;questionable observer detection problem;resolution variations;sensor noise;Clustering algorithms;Detection algorithms;Face;Face recognition;Feature extraction;Observers;Videos}, 

doi={10.1109/WACV.2011.5711501}, 

ISSN={1550-5790}, 

month={Jan},

}

R. Jillela, A. Ross, P. Flynn, Proc. of IEEE Workshop on Applications of Computer Vision (WACV), (Kona, USA), pp. 262-269, January 2011.

[pdf]

@INPROCEEDINGS{5711512, 

author={R. Jillela and A. Ross and P. J. Flynn}, 

booktitle={2011 IEEE Workshop on Applications of Computer Vision (WACV)}, 

title={Information fusion in low-resolution iris videos using Principal Components Transform}, 

year={2011}, 

volume={}, 

number={}, 

pages={262-269}, 

keywords={feature extraction;image fusion;image resolution;iris recognition;principal component analysis;transforms;video signal processing;discriminatory information extraction;image-level fusion scheme;information fusion;low-resolution iris video frame;low-resolution probe image;multibiometric grand challenge near infrared iris database;principal components transform;recognition accuracy;recognition performance;Face;Image reconstruction;Image resolution;Iris recognition;Pixel;Principal component analysis;Videos;Image averaging;Imagelevel fusion;Iris recognition;Low-resolution;Principal Components Transform (PCT)}, 

doi={10.1109/WACV.2011.5711512}, 

ISSN={1550-5790}, 

month={Jan},

}

Larry Shoemaker, Robert Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, pp. 259-290, January 2011.

[pdf]

@Article{Shoemaker2011,

author="Shoemaker, Larry

and Banfield, Robert E.

and Hall, Lawrence O.

and Bowyer, Kevin W.

and Kegelmeyer, W. Philip",

title="Detecting and ordering salient regions",

journal="Data Mining and Knowledge Discovery",

year="2011",

month="Jan",

day="01",

volume="22",

number="1",

pages="259--290",

abstract="We describe an ensemble approach to learning salient regions from arbitrarily partitioned data. The partitioning comes from the distributed processing requirements of large-scale simulations. The volume of the data is such that classifiers can train only on data local to a given partition. Since the data partition reflects the needs of the simulation, the class statistics can vary from partition to partition. Some classes will likely be missing from some or even most partitions. We combine a fast ensemble learning algorithm with scaled probabilistic majority voting in order to learn an accurate classifier from such data. Since some simulations are difficult to model without a considerable number of false positive errors, and since we are essentially building a search engine for simulation data, we order predicted regions to increase the likelihood that most of the top-ranked predictions are correct (salient). Results from simulation runs of a canister being torn and from a casing being dropped show that regions of interest are successfully identified in spite of the class imbalance in the individual training sets. Lift curve analysis shows that the use of data driven ordering methods provides a statistically significant improvement over the use of the default, natural time step ordering. Significant time is saved for the end user by allowing an improved focus on areas of interest without the need to conventionally search all of the data.",

issn="1573-756X",

doi="10.1007/s10618-010-0194-6",

url="https://doi.org/10.1007/s10618-010-0194-6"

}

P. Jonathon Phillips, Patrick Flynn, Kevin Bowyer, Richard Vorder Bruegge, Patrick Grother, George Quinn, Matthew Pruitt, Proc. 2011 IEEE International Conference on Automatic Face and Gesture Recognition (FG 2011), pp. 185-192, 2011.

[pdf]

@INPROCEEDINGS{5771395, 
author={P. J. Phillips and P. J. Flynn and K. W. Bowyer and R. W. V. Bruegge and P. J. Grother and G. W. Quinn and M. Pruitt}, 
booktitle={Face and Gesture 2011}, 
title={Distinguishing identical twins by face recognition}, 
year={2011}, 
volume={}, 
number={}, 
pages={185-192}, 
keywords={biometrics (access control);covariance analysis;face recognition;image matching;bootstrap method;covariate analysis;face recognition;identical twins;image dataset;multiple biometric evaluation;recognition experiment;still face track;Algorithm design and analysis;Error analysis;Face;Face recognition;Iris recognition;Lighting;Mobile communication}, 
doi={10.1109/FG.2011.5771395}, 
ISSN={}, 
month={March},

}

S. Biswas, G.  Aggarwal, P.J. Flynn, Proc. CVPR, pp. 601-608, 2011.

[pdf]

@ARTICLE{6494574, 

author={S. Biswas and G. Aggarwal and P. J. Flynn and K. W. Bowyer}, 

journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 

title={Pose-Robust Recognition of Low-Resolution Face Images}, 

year={2013}, 

volume={35}, 

number={12}, 

pages={3037-3049}, 

keywords={image classification;image matching;image recognition;object tracking;pose estimation;tensors;video surveillance;MultiPIE dataset;classifier-based approaches;face matching algorithms;facial landmark localization;frontal pose images;high-quality gallery images;high-resolution images;low-resolution face images;low-resolution uncontrolled probe images;multidimensional scaling;poor quality probe images;pose-robust recognition;super-resolution;surveillance cameras;surveillance imagery;surveillance quality facial image matching;surveillance video recognition;surveillance video tracking;tensor analysis;Cameras;Facial recognition;Iterative methods;Resolution;Surveillance;Face recognition;iterative majorization;low-resolution matching;multidimensional scaling;0}, 

doi={10.1109/TPAMI.2013.68}, 

ISSN={0162-8828}, 

month={Dec},

}

Larry Shoemaker, Robert Banfield, Lawrence O. Hall, Kevin W. Bowyer and W. Philip Kegelmeyer, Data Mining and Knowledge Discovery 12 (1-2), 259-290, January 2011.

[pdf]

@Article{Shoemaker2011,

author="Shoemaker, Larry

and Banfield, Robert E.

and Hall, Lawrence O.

and Bowyer, Kevin W.

and Kegelmeyer, W. Philip",

title="Detecting and ordering salient regions",

journal="Data Mining and Knowledge Discovery",

year="2011",

month="Jan",

day="01",

volume="22",

number="1",

pages="259--290",

abstract="We describe an ensemble approach to learning salient regions from arbitrarily partitioned data. The partitioning comes from the distributed processing requirements of large-scale simulations. The volume of the data is such that classifiers can train only on data local to a given partition. Since the data partition reflects the needs of the simulation, the class statistics can vary from partition to partition. Some classes will likely be missing from some or even most partitions. We combine a fast ensemble learning algorithm with scaled probabilistic majority voting in order to learn an accurate classifier from such data. Since some simulations are difficult to model without a considerable number of false positive errors, and since we are essentially building a search engine for simulation data, we order predicted regions to increase the likelihood that most of the top-ranked predictions are correct (salient). Results from simulation runs of a canister being torn and from a casing being dropped show that regions of interest are successfully identified in spite of the class imbalance in the individual training sets. Lift curve analysis shows that the use of data driven ordering methods provides a statistically significant improvement over the use of the default, natural time step ordering. Significant time is saved for the end user by allowing an improved focus on areas of interest without the need to conventionally search all of the data.",

issn="1573-756X",

doi="10.1007/s10618-010-0194-6",

url="https://doi.org/10.1007/s10618-010-0194-6"

}

Feng Si, Baojun Lin, Shancong Zhang, YF He, GH Zhao, CM Lv, LL Guo, F Wang, J Han, M Kamber, AK Jain, MN Murty, PJ Flynn, P Hansen, B Jaumard, L Kaufman, PJ Rousseeuw, K Kira, LA Rendell, L Parsons, E Haque, H Liu, G Karypis, EH Han, V Kumar, R Agrawal, J Gehrke, D Gunopulos, P Raghavan, RLF Cordeiro, AJ Traina, C Faloutsos, C Traina, B Singh, N Kushwaha, OP Vyas, I Kononenko, K Ye, K Feenstra, JA Heringa, AP Ijzerman, E Marchiori, CC Aggarwal, A Hinneburg, DA Keim, Journal of Software Engineering, orgz, vol. 11, pp. 1163-1170, 2011.

[pdf]

@InProceedings{10.1007/3-540-44503-X_27,

author="Aggarwal, Charu C.

and Hinneburg, Alexander

and Keim, Daniel A.",

editor="Van den Bussche, Jan

and Vianu, Victor",

title="On the Surprising Behavior of Distance Metrics in High Dimensional Space",

booktitle="Database Theory --- ICDT 2001",

year="2001",

publisher="Springer Berlin Heidelberg",

address="Berlin, Heidelberg",

pages="420--434",

abstract="In recent years, the effect of the curse of high dimensionality has been studied in great detail on several problems such as clustering, nearest neighbor search, and indexing. In high dimensional space the data becomes sparse, and traditional indexing and algorithmic techniques fail from a effciency and/or effectiveness perspective. Recent research results show that in high dimensional space, the concept of proximity, distance or nearest neighbor may not even be qualitatively meaningful. In this paper, we view the dimensionality curse from the point of view of the distance metrics which are used to measure the similarity between objects. We specifically examine the behavior of the commonly used L                k               norm and show that the problem of meaningfulness in high dimensionality is sensitive to the value of k. For example, this means that the Manhattan distance metric L(1                      norm) is consistently more preferable than the Euclidean distance metric L(2                      norm) for high dimensional data mining applications. Using the intuition derived from our analysis, we introduce and examine a natural extension of the L                k               norm to fractional distance metrics. We show that the fractional distance metric provides more meaningful results both from the theoretical and empirical perspective. The results show that fractional distance metrics can significantly improve the effectiveness of standard clustering algorithms such as the k-means algorithm.",

isbn="978-3-540-44503-6"

}

2010

Joseph W Thompson, Patrick J Flynn, Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on, IEEE, September 2010.

[pdf]

@INPROCEEDINGS{5634499, 

author={J. W. Thompson and P. J. Flynn}, 

booktitle={2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)}, 

title={A segmentation perturbation method for improved iris recognition}, 

year={2010}, 

volume={}, 

number={}, 

pages={1-8}, 

keywords={image segmentation;iris recognition;improved iris recognition;perturbation match score distributions;segmentation perturbation method;Active contours;Eyelids;Hamming distance;Image segmentation;Iris;Iris recognition;Probes}, 

doi={10.1109/BTAS.2010.5634499}, 

ISSN={}, 

month={Sept},}

Presentation only of work done with Karen Hollingsworth, Steve Lagree, Sam Fenker and Patrick J. Flynn, Biometrics Consortium Conference (BCC), September 2010, Tampa, FL.

[slides]