GSCL: Generative Self-Supervised Contrastive Learning for Vein-Based Biometric Verification

Abstract

Vein-based biometric technology offers secure identity authentication due to the concealed nature of blood vessels. Despite the promising performance of deep learning-based biometric vein recognition, the scarcity of vein data hinders the discriminative power of deep features, thus affecting overall performance. To tackle this problem, this paper presents a generative self-supervised contrastive learning (GSCL) scheme, designed from a data-centric viewpoint to fully mine the potential prior knowledge from limited vein data for improving feature representations. GSCL first utilizes a style-based generator to model vein image distribution and then generate numerous vein image samples. These generated vein images are then leveraged to pretrain the feature extraction network via self-supervised contrastive learning. Subsequently, the network undergoes further fine-tuning using the original training data in a supervised manner. This systematic combination of generative and discriminative modeling allows the network to comprehensively excavate the semantic prior knowledge inherent in vein data, ultimately improving the quality of feature representations. In addition, we investigate a multi-template enrollment method for improving practical verification accuracy. Extensive experiments conducted on public finger vein and palm vein databases, as well as a newly collected finger vein video database, demonstrate the effectiveness of GSCL in improving representation quality.