  Face anti-spoofing (FAS) plays an important role in protecting face
recognition systems from face representation attacks. Many recent studies in
FAS have approached this problem with domain generalization technique. Domain
generalization aims to increase generalization performance to better detect
various types of attacks and unseen attacks. However, previous studies in this
area have defined each domain simply as an anti-spoofing datasets and focused
on developing learning techniques. In this paper, we proposed a method that
enables network to judge its domain by itself with the clustered convolutional
feature statistics from intermediate layers of the network, without labeling
domains as datasets. We obtained pseudo-domain labels by not only using the
network extracting features, but also using depth estimators, which were
previously used only as an auxiliary task in FAS. In our experiments, we
trained with three datasets and evaluated the performance with the remaining
one dataset to demonstrate the effectiveness of the proposed method by
conducting a total of four sets of experiments.
