HDC-Net: A Heterogeneous Dual-Stream Network with Supervised Contrastive Learning for Contactless Palmprint and Palm Vein Fusion Recognition

With the advancement of contactless biometric technologies, improving recognition accuracy and robustness in unconstrained environments remains a significant challenge. To address insufficient feature representation caused by modality discrepancies, as well as non-compact feature distributions, we propose HDC-Net, a contactless palmprint and palm vein fusion recognition model based on a heterogeneous dual-stream network and supervised contrastive learning. Specifically, a heterogeneous dual-stream feature extraction architecture is designed to learn modality-specific representations from palmprint and palm vein images. Supervised contrastive learning is introduced to enhance intra-class compactness and improve inter-class separability. Furthermore, a cross-modal interaction fusion module is developed to facilitate complementary feature learning across modalities. Experimental results on Tongji, CASIA-MS, and the self-built SCAU-PM dataset demonstrate that the proposed method achieves equal error rates (EERs) of 0.05%, 0.21%, and 0.02%, respectively. These results indicate that the proposed method achieves reliable recognition performance across different datasets and provides a feasible approach for contactless palmprint and palm vein fusion recognition.