  Face anti-spoofing (FAS) is crucial for securing face recognition systems.
However, existing FAS methods with handcrafted binary or pixel-wise labels have
limitations due to diverse presentation attacks (PAs). In this paper, we
propose an attack type robust face anti-spoofing framework under light flash,
called ATR-FAS. Due to imaging differences caused by various attack types,
traditional FAS methods based on single binary classification network may
result in excessive intra-class distance of spoof faces, leading to a challenge
of decision boundary learning. Therefore, we employed multiple networks to
reconstruct multi-frame depth maps as auxiliary supervision, and each network
experts in one type of attack. A dual gate module (DGM) consisting of a type
gate and a frame-attention gate is introduced, which perform attack type
recognition and multi-frame attention generation, respectively. The outputs of
DGM are utilized as weight to mix the result of multiple expert networks. The
multi-experts mixture enables ATR-FAS to generate spoof-differentiated depth
maps, and stably detects spoof faces without being affected by different types
of PAs. Moreover, we design a differential normalization procedure to convert
original flash frames into differential frames. This simple but effective
processing enhances the details in flash frames, aiding in the generation of
depth maps. To verify the effectiveness of our framework, we collected a
large-scale dataset containing 12,660 live and spoof videos with diverse PAs
under dynamic flash from the smartphone screen. Extensive experiments
illustrate that the proposed ATR-FAS significantly outperforms existing
state-of-the-art methods. The code and dataset will be available at
https://github.com/Chaochao-Lin/ATR-FAS.
