  Face anti-spoofing (FAS) is indispensable for a face recognition system. Many
texture-driven countermeasures were developed against presentation attacks
(PAs), but the performance against unseen domains or unseen spoofing types is
still unsatisfactory. Instead of exhaustively collecting all the spoofing
variations and making binary decisions of live/spoof, we offer a new
perspective on the FAS task to distinguish between normal and abnormal
movements of live and spoof presentations. We propose Geometry-Aware
Interaction Network (GAIN), which exploits dense facial landmarks with
spatio-temporal graph convolutional network (ST-GCN) to establish a more
interpretable and modularized FAS model. Additionally, with our cross-attention
feature interaction mechanism, GAIN can be easily integrated with other
existing methods to significantly boost performance. Our approach achieves
state-of-the-art performance in the standard intra- and cross-dataset
evaluations. Moreover, our model outperforms state-of-the-art methods by a
large margin in the cross-dataset cross-type protocol on CASIA-SURF 3DMask
(+10.26% higher AUC score), exhibiting strong robustness against domain shifts
and unseen spoofing types.
