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.

翻译：本研究提出了一种前沿的再平衡对比学习策略，旨在增强人脸识别系统中的人脸防伪能力，重点应对打印照片及高仿真硅胶或乳胶面具带来的挑战。我们的方法利用受益于快照光谱成像技术、可提供高光谱图像的HySpeFAS数据集，将类别级对比学习与数据重采样及一种创新的面向真实人脸的再加权技术相结合。该方法有效缓解了数据集不平衡问题，并减少了与身份相关的偏差。值得注意的是，我们的策略在HySpeFAS数据集上实现了前所未有的0.0000%平均分类错误率（ACER），在CVPR 2024的Chalearn快照光谱成像人脸防伪挑战赛中位列第一。