  In this paper, we present a novel architecture that integrates a deep hashing
framework with a neural network decoder (NND) for application to face template
protection. It improves upon existing face template protection techniques to
provide better matching performance with one-shot and multi-shot enrollment. A
key novelty of our proposed architecture is that the framework can also be used
with zero-shot enrollment. This implies that our architecture does not need to
be re-trained even if a new subject is to be enrolled into the system. The
proposed architecture consists of two major components: a deep hashing (DH)
component, which is used for robust mapping of face images to their
corresponding intermediate binary codes, and a NND component, which corrects
errors in the intermediate binary codes that are caused by differences in the
enrollment and probe biometrics due to factors such as variation in pose,
illumination, and other factors. The final binary code generated by the NND is
then cryptographically hashed and stored as a secure face template in the
database. The efficacy of our approach with zero-shot, one-shot, and multi-shot
enrollments is shown for CMU-PIE, Extended Yale B, WVU multimodal and Multi-PIE
face databases. With zero-shot enrollment, the system achieves approximately
85% genuine accept rates (GAR) at 0.01% false accept rate (FAR), and with
one-shot and multi-shot enrollments, it achieves approximately 99.95% GAR at
0.01% FAR, while providing a high level of template security.
