We have been collecting data sets and conducting baseline and advanced personal identification studies using biometric measurements. We are committed to releasing all data collated to eligible research groups, with appropriate controls to forbid on-line distribution outside the research community. Data is distributed using rsync.
If you are interested in obtaining any of the biometric datasets described below, please follow these instructions:
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Here, we present a new dataset for the ReID problem, known as the 'Electronic Be-On-the-LookOut' (EBOLO) dataset. This dataset was collected from a camera network operated by the Greater Cleveland Regional Transportation Authority. The data was collected by students and faculty from Notre Dame, Rensselaer, Purdue, and Columbia. All data was collected under the terms of a human subjects IRB data collection protocol and all actors in the dataset signed appropriate consent forms at each collection session. The head of GCRTA has ruled that this data is a public record.
Advances in image restoration and enhancement techniques have led to discussion about how such algorithms can be applied as a pre-processing step to improve automatic visual recognition. In principle, techniques like deblurring and super-resolution should yield improvements by de-emphasizing noise and increasing signal in an input image. But the historically divergent goals of computational photography and visual recognition communities have created a significant need for more work in this direction. To facilitate new research, we introduce a new benchmark dataset called UG^2, which contains three difficult real-world scenarios: uncontrolled videos taken by UAVs and manned gliders, as well as controlled videos taken on the ground.
Data Type: Synthetic Face Images, 3D Head Models
Approximate Download Size: 211 GB
The dataset contains two types of data:
1. A set of 3D head models (.abs files) and their corresponding 2D RGB registration image (.ppm files), obtained using a Konica-Minolta ‘Vivid 910’ 3D scanner, of real identities (subjects), either Male or Female in gender, and Caucasian or Asian in ethnicity.
2. A set of RGB face images, masked faces without context and background 800x600 in size, of fully synthetic subjects (identities) that do not exist in reality. The synthetic identities are generated by consistent sampling of facial parts from face images of different real identities, sampled from, either Male or Female in gender, and Caucasian or Asian in ethnicity.
Since all the identities in this dataset are synthetic, i.e. they do not exist, they can be used freely without any privacy concerns. These synthetic face images were generated using Python and OpenGL, with minimal training, and can be used as – (1) supplemental training data to train CNNs, (2) additional distractor face images in the gallery for face verification experiments.