  Face anti-spoofing (FAS) based on domain generalization (DG) has been
recently studied to improve the generalization on unseen scenarios. Previous
methods typically rely on domain labels to align the distribution of each
domain for learning domain-invariant representations. However, artificial
domain labels are coarse-grained and subjective, which cannot reflect real
domain distributions accurately. Besides, such domain-aware methods focus on
domain-level alignment, which is not fine-grained enough to ensure that learned
representations are insensitive to domain styles. To address these issues, we
propose a novel perspective for DG FAS that aligns features on the instance
level without the need for domain labels. Specifically, Instance-Aware Domain
Generalization framework is proposed to learn the generalizable feature by
weakening the features' sensitivity to instance-specific styles. Concretely, we
propose Asymmetric Instance Adaptive Whitening to adaptively eliminate the
style-sensitive feature correlation, boosting the generalization. Moreover,
Dynamic Kernel Generator and Categorical Style Assembly are proposed to first
extract the instance-specific features and then generate the style-diversified
features with large style shifts, respectively, further facilitating the
learning of style-insensitive features. Extensive experiments and analysis
demonstrate the superiority of our method over state-of-the-art competitors.
Code will be publicly available at https://github.com/qianyuzqy/IADG.
