FEHash: Full Entropy Hash for Face Template Protection
In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020-06-01
Online
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Zugriff:
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.
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FEHash: Full Entropy Hash for Face Template Protection
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Autor/in / Beteiligte Person: | Nguyen, Thuc D. ; Lam Dai Tran ; Dang, Thao ; Choi, Deokjai |
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Zeitschrift: | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020-06-01 |
Veröffentlichung: | IEEE, 2020 |
Medientyp: | unknown |
DOI: | 10.1109/cvprw50498.2020.00413 |
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