Deep Bio-Hashing Network for Privacy-Preserving Cancelable Finger Vein Recognition

Finger vein recognition technology has become one of the primary solutions for high-security identification systems. However, traditional finger vein recognition methods face several limitations, such as the risk of permanent identity loss due to biometric data leakage. Through designing cancelable biometrics, users' privacy and security can be further protected, and the risk of biometric data misuse can be reduced. In this paper, we propose Deep Bio-Hashing Network (DBHN) for privacy-preserving finger vein recognition, achieving end-to-end cancelable recognition. Specifically, we design a class center alignment module to improve feature alignment, which aligns the variations of all potential finger views with the finger center view via a learnable transformation. Furthermore, to tackle the security problem caused by stolen tokenized random numbers, a Deep Bio-Hashing layer is proposed, which utilizes a system-level token instead of assigning unique tokens to each user. To supervise the learning process of DBHN, we design a hybrid loss function including classification loss, consistency-based localization loss, and class center triplet loss. Finally, we conduct experiments and analysis on three publicly available datasets. Experimental results show that our method has favorable recognition performance and achieves competitive results compared to state-of-the-art hash-based methods. The analysis verifies the cancelable biometrics attributes and justifies the resilience of the method against existing security and privacy attacks.