Adversarial Learning Domain-Invariant Conditional Features for Robust Face Anti-spoofing

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.