FEHash: Full Entropy Hash for Face Template Protection
| Autor: | Thuc D. Nguyen, Lam Dai Tran, Thao Dang, Deokjai Choi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021110 strategic
defence & security studies Biometrics Computer science business.industry Hash function 0211 other engineering and technologies Cryptography 02 engineering and technology computer.software_genre Facial recognition system Locality-sensitive hashing Support vector machine 0202 electrical engineering electronic engineering information engineering Entropy (information theory) 020201 artificial intelligence & image processing Data mining Artificial intelligence Security level business computer |
| Zdroj: | CVPR Workshops |
| DOI: | 10.1109/cvprw50498.2020.00413 |
| Popis: | 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, andFERET 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. |
| Databáze: | OpenAIRE |
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