  Face anti-spoofing (FAS) seeks to discriminate genuine faces from fake ones
arising from any type of spoofing attack. Due to the wide varieties of attacks,
it is implausible to obtain training data that spans all attack types. We
propose to leverage physical cues to attain better generalization on unseen
domains. As a specific demonstration, we use physically guided proxy cues such
as depth, reflection, and material to complement our main anti-spoofing (a.k.a
liveness detection) task, with the intuition that genuine faces across domains
have consistent face-like geometry, minimal reflection, and skin material. We
introduce a novel uncertainty-aware attention scheme that independently learns
to weigh the relative contributions of the main and proxy tasks, preventing the
over-confident issue with traditional attention modules. Further, we propose
attribute-assisted hard negative mining to disentangle liveness-irrelevant
features with liveness features during learning. We evaluate extensively on
public benchmarks with intra-dataset and inter-dataset protocols. Our method
achieves the superior performance especially in unseen domain generalization
for FAS.
