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Single-shot face anti-spoofing (FAS) is a key technique for securing facerecognition systems, and it requires only static images as input. However,single-shot FAS remains a challenging and under-explored problem due to twomain reasons: 1) on the data side, learning FAS from RGB images is largelycontext-dependent, and single-shot images without additional annotationscontain limited semantic information. 2) on the model side, existingsingle-shot FAS models are infeasible to provide proper evidence for theirdecisions, and FAS methods based on depth estimation require expensiveper-pixel annotations. To address these issues, a large binocular NIR imagedataset (BNI-FAS) is constructed and published, which contains more than300,000 real face and plane attack images, and an Interpretable FAS Transformer(IFAST) is proposed that requires only weak supervision to produceinterpretable predictions. Our IFAST can produce pixel-wise disparity maps bythe proposed disparity estimation Transformer with Dynamic Matching Attention(DMA) block. Besides, a well-designed confidence map generator is adopted tocooperate with the proposed dual-teacher distillation module to obtain thefinal discriminant results. The comprehensive experiments show that our IFASTcan achieve state-of-the-art results on BNI-FAS, proving the effectiveness ofthe single-shot FAS based on binocular NIR images.

IFAST: Weakly Supervised Interpretable Face Anti-spoofing from Single-shot Binocular NIR Images