Abstract
Face anti-spoofing has been widely exploited in recent years to ensure security in face recognition systems; however, this technology suffers from poor generalization performance on unseen samples. Most previous methods align the marginal distributions from multiple source domains to learn domain-invariant features to mitigate domain shift. However, the category information of samples from different domains is ignored during these marginal distribution alignments; this can potentially lead to features of one category from one domain being misaligned to those of different categories from other domains, although the marginal distributions across domains are well aligned from the whole point of view. In this paper, we propose a simple but effective conditional domain adversarial framework whose main goal is to align the conditional distributions across domains to learn domain-invariant conditional features. Specifically, we first construct a parallel domain structure and its corresponding regularization to reduce negative influences from the finite samples and diversity of spoof face images on the conditional distribution alignments. Then, based on the parallel domain structure, a feature extractor and a global domain classifier, which play a conditional domain adversarial game, are leveraged to make the features of the same category across different domains indistinguishable. Moreover, intra-domain and cross-domain discrimination regularization are further exploited in conjunction with conditional domain adversarial training to minimize the classification error of class predictors. Extensive qualitative and quantitative experiments demonstrate that the proposed method learns well-generalized features from fewer source domains and achieves state-of-the-art performance on six public datasets.
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Data Availibility Statement
The data that support the findings of this study are available from the authors upon reasonable request.
Notes
Datasets are treated as domains in this paper.
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Acknowledgements
This work was supported in part by the National Key Research and Development Program of China under Grant No. 2022YFC3310400, in part by the Natural Science Foundation of China (Grant Nos. 62106247, 62276263 and 62076240), in part by the Scientific Research Foundation of Department of Education of Hunan Province (Grant No. 22B0439), in part by the Beijing Municipal Natural Science Foundation (Grant No. 4222054).
Funding
This work was also supported in part by the Natural Science Foundation of China (Grant Nos. 62276263) and in part by the Beijing Municipal Natural Science Foundation (Grant No. 4222054).
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Communicated by Zhouchen Lin.
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Jiang, F., Li, Q., Liu, P. et al. Adversarial Learning Domain-Invariant Conditional Features for Robust Face Anti-spoofing. Int J Comput Vis 131, 1680–1703 (2023). https://doi.org/10.1007/s11263-023-01778-x
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DOI: https://doi.org/10.1007/s11263-023-01778-x