Abstract
Face Anti-Spoofing (FAS) plays a critical role in safeguarding face recognition systems, while previous FAS methods suffer from poor generalization when applied to unseen domains. Although recent methods have made progress via domain generalization technology, they are still sensitive to variations in face quality caused by task-irrelevant factors like camera and illumination. In this paper, we propose a novel Quality-Invariant Domain Generalization method (QIDG) with a teacher-student architecture, which aligns liveness features into a quality-invariant space to alleviate interference from task-irrelated factors. Specifically, QIDG utilizes the teacher model to produce face quality representations, which serve as the guidance for the student model to explore the quality-invariant space. To seek this space, the student model devises two novel modules, i.e., a dual adversarial learning module (DAL) and a quality feature assembly module (QFA). The former produces domain-invariant liveness features and task-irrelated quality features. While the latter assembles these two features from the same faces into complete quality representations, as well as assembles these two features from living faces in different domains. In this way, QIDG not only achieves the alignment of the domain-invariant liveness features to the quality-invariant space, but also promotes compactness of living faces from different domains in the feature space. Extensive cross-domain experiments demonstrate the superiority of our method on five public databases.
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The data that support the findings of this study are available from the authors upon reasonable request.
Notes
In this paper, target domains refer the unseen testing datasets, while training datasets are regarded as source domains.
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Acknowledgements
This work was funded by the National Natural Science Foundation of China under Grant Nos. 62236010, 62306022, 62306021, China Postdoctoral Science Foundation under Grant No. 2022M720318, and Beijing Natural Science Foundation under Grant No. L233008.
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Liu, Y., Li, Z., Xu, Y. et al. Quality-Invariant Domain Generalization for Face Anti-Spoofing. Int J Comput Vis 132, 5239–5254 (2024). https://doi.org/10.1007/s11263-024-02092-w
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DOI: https://doi.org/10.1007/s11263-024-02092-w