A Random-Binding-Based Bio-Hashing Template Protection Method for Palm Vein Recognition

To mitigate the risk of data breaches, an increasing number of biometric recognition systems are introducing encryption biometric template protection methods and directly matching in the encrypted domain. Depending on the approach to key management, prevailing biometric template protection strategies can be categorized into declarative and distributive methods. The former are challenged by complexities and vulnerabilities linked to key loss, while the latter are compromised by fixed mapping rules that may expose personal information. We present a biometric template protection method that combines random-fixed factors to handle these challenges, thereby protecting the user’s biometric privacy. Firstly, we introduce a random activation factor generation module that extracts scaling and offset factors from the user’s biometric data. This module randomly binds factors to different positions in each authentication process, rendering distance-dependent bitwise cracking algorithms ineffective. Secondly, we propose a fixed multi-branch mapping module that enhances feature expression and minimizes information loss post-encryption. We also develop a trainable min-max hash method, optimized using an improved approximate contrastive loss. Employing palm veins as a case study, we conducted experiments across five datasets, where our method outperformed other encrypted domain methods and showed competitive advantages over mainstream non-encrypted methods. Moreover, we have demonstrated that our method ensures robust performance while meeting essential security requirements of irreversibility, unlinkability, and revocability.