Anomaly Metrics on Class Variations For Face Anti-Spoofing

In face anti-spoofing tasks, distinguishing between live and spoof faces across different data domains presents challenges due to inter-class similarities, intra-class variations and unknown spoof patterns. This hampers generalization in real-world applications. To address this, we propose a novel convolutional neural network framework that utilizes spatial-frequency cues for 2D and 3D attacks. Furthermore, we introduce compact anomaly metrics and design three anomaly metrics-based supervisions from the perspective of Reed-Xiaoli anomaly detection, aiming to tackle the challenge posed by unknown attacks. Thanks to our proposed spatial frequency factorization network and its frequency-related supervisions, the spoofing cues are significantly enhanced, resulting in remarkable improvements in our experimental results. These outcomes demonstrate that our proposed framework achieves state-of-the-art performance on both monocular and multi-spectral benchmark datasets.