Instance-Aware Domain Generalization for Face Anti-Spoofing

• URL: http://arxiv.org/abs/2304.05640v1
• Date: Wed, 12 Apr 2023 06:41:16 GMT
• Title: Instance-Aware Domain Generalization for Face Anti-Spoofing
• Authors: Qianyu Zhou, Ke-Yue Zhang, Taiping Yao, Xuequan Lu, Ran Yi, Shouhong Ding, Lizhuang Ma
• Abstract summary: Face anti-spoofing (FAS) has been recently studied to improve the generalization on unseen scenarios. Previous methods rely on domain labels to align the distribution of each domain for learning domain-invariant representations. We propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels.
• Score: 42.36157210235893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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