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

Summary

This paper proposes MCCENet, a deep multimodal fusion network for palm authentication that combines palm vein (PV) and palm shape (PS) data. The authors observe that discriminative palm shape contour information is mainly captured in shallow layers, so they design a hierarchical feature fusion (HFF) module for inter-modal channel exchange at those layers, plus a multimodal contrastive learning loss to align features across modalities. Experiments on eight public datasets report state-of-the-art performance in all cases.