TY - GEN
T1 - Deep Secure Encoding for Face Template Protection
AU - Pandey, Rohit Kumar
AU - Zhou, Yingbo
AU - Kota, Bhargava Urala
AU - Govindaraju, Venu
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/16
Y1 - 2016/12/16
N2 - In this paper we present a framework for secure identification using deep neural networks, and apply it to the task of template protection for face password authentication. We use deep convolutional neural networks (CNNs) to learn a mapping from face images to maximum entropy binary (MEB) codes. The mapping is robust enough to tackle the problem of exact matching, yielding the same code for new samples of a user as the code assigned during training. These codes are then hashed using any hash function that follows the random oracle model (like SHA-512) to generate protected face templates. The algorithm makes no unrealistic assumptions and offers high template security, cancelability, and matching performance comparable to the state-of-the-art. The efficacy of the approach is shown on CMU-PIE, Extended Yale B, and Multi-PIE face databases. We achieve high (~ 95%) genuine accept rates (GAR) at zero false accept rate (FAR) while maintaining a high level of template security.
AB - In this paper we present a framework for secure identification using deep neural networks, and apply it to the task of template protection for face password authentication. We use deep convolutional neural networks (CNNs) to learn a mapping from face images to maximum entropy binary (MEB) codes. The mapping is robust enough to tackle the problem of exact matching, yielding the same code for new samples of a user as the code assigned during training. These codes are then hashed using any hash function that follows the random oracle model (like SHA-512) to generate protected face templates. The algorithm makes no unrealistic assumptions and offers high template security, cancelability, and matching performance comparable to the state-of-the-art. The efficacy of the approach is shown on CMU-PIE, Extended Yale B, and Multi-PIE face databases. We achieve high (~ 95%) genuine accept rates (GAR) at zero false accept rate (FAR) while maintaining a high level of template security.
UR - https://www.scopus.com/pages/publications/85010204683
U2 - 10.1109/CVPRW.2016.17
DO - 10.1109/CVPRW.2016.17
M3 - Conference contribution
AN - SCOPUS:85010204683
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 77
EP - 83
BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
PB - IEEE Computer Society
T2 - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
Y2 - 26 June 2016 through 1 July 2016
ER -