  With various face presentation attacks arising under unseen scenarios, face
anti-spoofing (FAS) based on domain generalization (DG) has drawn growing
attention due to its robustness. Most existing methods utilize DG frameworks to
align the features to seek a compact and generalized feature space. However,
little attention has been paid to the feature extraction process for the FAS
task, especially the influence of normalization, which also has a great impact
on the generalization of the learned representation. To address this issue, we
propose a novel perspective of face anti-spoofing that focuses on the
normalization selection in the feature extraction process. Concretely, an
Adaptive Normalized Representation Learning (ANRL) framework is devised, which
adaptively selects feature normalization methods according to the inputs,
aiming to learn domain-agnostic and discriminative representation. Moreover, to
facilitate the representation learning, Dual Calibration Constraints are
designed, including Inter-Domain Compatible loss and Inter-Class Separable
loss, which provide a better optimization direction for generalizable
representation. Extensive experiments and visualizations are presented to
demonstrate the effectiveness of our method against the SOTA competitors.
