  Although previous CNN based face anti-spoofing methods have achieved
promising performance under intra-dataset testing, they suffer from poor
generalization under cross-dataset testing. The main reason is that they learn
the network with only binary supervision, which may learn arbitrary cues
overfitting on the training dataset. To make the learned feature explainable
and more generalizable, some researchers introduce facial depth and reflection
map as the auxiliary supervision. However, many other generalizable cues are
unexplored for face anti-spoofing, which limits their performance under
cross-dataset testing. To this end, we propose a novel framework to learn
multiple explainable and generalizable cues (MEGC) for face anti-spoofing.
Specifically, inspired by the process of human decision, four mainly used cues
by humans are introduced as auxiliary supervision including the boundary of
spoof medium, moir\'e pattern, reflection artifacts and facial depth in
addition to the binary supervision. To avoid extra labelling cost,
corresponding synthetic methods are proposed to generate these auxiliary
supervision maps. Extensive experiments on public datasets validate the
effectiveness of these cues, and state-of-the-art performances are achieved by
our proposed method.
