FVRFL: Federated Learning for Finger Vein Recognition Under Distributed Scenario

Hengyi Ren, Lijuan Sun, Ying Cao, Liang Qi, Qiaolin Ye

Abstract: The deep-learning-based finger vein recognition method has achieved excellent recognition performance in local tasks. However, in distributed scenarios where the method is deployed on terminal devices, the recognition performance experiences a significant decline for new users who have not been included in the model’s training set. One feasible solution is to equip the model with the capability of continual learning. Nevertheless, due to the increasing concerns over data privacy issues and legal restrictions, it becomes challenging for the recognition system to access vein images from different terminal devices to continuously improve the recognition model. In this work, to mitigate the conflict between model learning and data privacy, we propose a finger vein recognition framework based on federated learning, called FVRFL, which gives the recognition model sustainable learning ability in a privacy-aware manner. Furthermore, to tackle the challenge of user diversity in finger vein recognition terminals, we propose a modular optimization strategy. This strategy optimizes personalized models for different terminals and designs target functions for distinct modules, catering to the high-performance requirements of terminal users for the recognition system. Extensive experiments on public datasets demonstrate that the proposed method not only empowers the recognition model with the ability of continual learning, improving the performance of the recognition system on terminal devices, but also effectively protects the privacy of user vein templates.