FEHash: Full Entropy Hash for Face Template Protection

Thao M. Dang, Lam Tran, Thuc Dinh Nguyen, Deokjai Choi

Abstract: In this paper, we present a hashing function for the application of face template protection, which improves the correctness of existing algorithms while maintaining the security simultaneously. The novel architecture constructed based on four components: a self-defined concept called padding people, Random Fourier Features, Support Vector Machine, and Locality Sensitive Hashing. The proposed method is trained, with one-shot and multi-shot enrollment, to encode the user's biometric data to a predefined output with high probability. The predefined hashing output is cryptographically hashed and stored as a secure face template. Predesigning outputs ensures the strict requirements of biometric cryptosystems, namely, randomness and unlinkability. We prove that our method reaches the REQ-WBP (Weak Biometric Privacy) security level, which implies irreversibility. The efficacy of our approach is evaluated on the widely used CMU-PIE, FEI, and FERET databases; our matching performances achieve 100% genuine acceptance rate at 0% false acceptance rate for all three databases and enrollment types. To our knowledge, our matching results outperform most of state-of-the-art results.