MCCENet: Multimodal Contrastive Learning Channel-Exchanging Networks for Palm Multimodal Authentication

This paper presents MCCENet, a deep learning system that improves biometric authentication by combining two types of palm data: palm vein patterns and palm shape. The system uses hierarchical feature fusion (a technique that exchanges information between different data types at early processing layers) and multimodal contrastive learning (a training method that helps the AI learn similar representations for related data from different sources) to better recognize individuals, achieving better accuracy than previous methods tested across eight public datasets.