  This study reveals a cutting-edge re-balanced contrastive learning strategy
aimed at strengthening face anti-spoofing capabilities within facial
recognition systems, with a focus on countering the challenges posed by printed
photos, and highly realistic silicone or latex masks. Leveraging the HySpeFAS
dataset, which benefits from Snapshot Spectral Imaging technology to provide
hyperspectral images, our approach harmonizes class-level contrastive learning
with data resampling and an innovative real-face oriented reweighting
technique. This method effectively mitigates dataset imbalances and reduces
identity-related biases. Notably, our strategy achieved an unprecedented
0.0000\% Average Classification Error Rate (ACER) on the HySpeFAS dataset,
ranking first at the Chalearn Snapshot Spectral Imaging Face Anti-spoofing
Challenge on CVPR 2024.
