BioDeepHash: Generating Consistent Templates for Secure Biometric Recognition

Given the immutability of biometric data, it is imperative to develop a biometric template protection method that guarantees the complete non-disclosure of any original biometric information while ensuring high recognition performance. Two mainstream approaches in biometric template protection—cancelable biometrics and biometric cryptosystems—have been widely adopted; however, the protected templates produced by these methods still contain some of the original biometric data, which can lead to privacy leakage. To address these challenges, we propose a novel framework named BioDeepHash that integrates deep hashing with cryptographic hash functions. In our approach, a deep hashing model generates consistent templates for similar biometric data from the same user, thereby eliminating intra-class variations. An application-specific XOR string is then used to achieve revocability, and finally these consistent templates are processed by cryptographic hash functions to produce protected templates that meet strict security standards. Our experimental results show that, compared with existing methods, BioDeepHash increases the average Genuine Acceptance Rate by 10.12% on the iris dataset and by 3.12% on the facial dataset, while achieving an extremely low False Acceptance Rate—0% for the iris dataset and only 0.0002% for the facial dataset.