Joint Learning of Identity and Vein Features for Enhanced Representations in Vascular Biometrics

Abstract

Vascular biometrics have shown great promise for secure authentication applications and have received increased attention in recent years. This paper proposes a novel framework for joint identity and segmentation feature learning to enrich representations and improve verification performance. The framework utilizes an encoder-decoder architecture, where the encoder is trained under metric learning supervision to extract discriminative identity features. Concurrently, the decoder is trained with vein mask segmentation supervision to extract vein pattern features. By jointly learning high-level identity features and low-level vein features in an end-to-end manner, the representations are enriched. We further design a bi-feature matching scheme utilizing score fusion to integrate both features for identity verification. Experiments conducted on public finger and palm vein datasets reveal that the proposed approach significantly improves verification accuracy, while introducing reasonable complexity overhead.