  Single-shot face anti-spoofing (FAS) is a key technique for securing face
recognition systems, and it requires only static images as input. However,
single-shot FAS remains a challenging and under-explored problem due to two
main reasons: 1) on the data side, learning FAS from RGB images is largely
context-dependent, and single-shot images without additional annotations
contain limited semantic information. 2) on the model side, existing
single-shot FAS models are infeasible to provide proper evidence for their
decisions, and FAS methods based on depth estimation require expensive
per-pixel annotations. To address these issues, a large binocular NIR image
dataset (BNI-FAS) is constructed and published, which contains more than
300,000 real face and plane attack images, and an Interpretable FAS Transformer
(IFAST) is proposed that requires only weak supervision to produce
interpretable predictions. Our IFAST can produce pixel-wise disparity maps by
the proposed disparity estimation Transformer with Dynamic Matching Attention
(DMA) block. Besides, a well-designed confidence map generator is adopted to
cooperate with the proposed dual-teacher distillation module to obtain the
final discriminant results. The comprehensive experiments show that our IFAST
can achieve state-of-the-art results on BNI-FAS, proving the effectiveness of
the single-shot FAS based on binocular NIR images.
