Face Retouching

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   Digitally altering, or retouching, face images is a common practice for images on social media, photo sharing websites, and even identification cards when the standards are not strictly enforced. In this project, we aim to develop algorithms of detecting retouching effects in faces. We also analyze the covariates of the problem and improve the approach to attain generalizability across different types of demography. For the purposes of this research, we also developed the largest existing non-celebrity retouched dataset with retouched and un-retouched images.

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Aparna Bharati, Kevin Bowyer

Collaborators: Mayank Vatsa, Richa Singh

Demography-Based Facial Retouching Detection Using Subclass Supervised Sparse Autoencoder, Aparna Bharati, Mayank Vatsa, Richa Singh, Kevin W. Bowyer, and Xin Tong. IEEE International Joint Conference on Biometrics (IJCB), pp. 474-482, 2017: [pdf]

Detecting Facial Retouching Using Supervised Deep Learning, 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]