Weighted Joint Distribution Optimal Transport Based Domain Adaptation for Cross-Scenario Face Anti-Spoofing

Unsupervised domain adaptation-based face anti-spoofing methods have attracted more and more attention due to their promising generalization abilities. To mitigate domain bias, existing methods generally attempt to align the marginal distributions of samples from source and target domains. However, the label and pseudo-label information of the samples from source and target domains are ignored. To solve this problem, this paper proposes a Weighted Joint Distribution Optimal Transport unsupervised multi-source domain adaptation method for cross-scenario face anti-spoofing (WJDOT-FAS). WJDOT-FAS consists of three modules: joint distribution estimation, joint distribution optimal transport, and domain weight optimization. Specifically, the joint distributions of the features and pseudo labels of multi-source and target domains are firstly estimated based on a pre-trained feature extractor and a randomly initialized classifier. Then, we compute the cost matrices and the optimal transportation mappings from the joint distributions related to each source domain and the target domain by solving Lp-L1 optimal transport problems. Finally, based on the loss functions of different source domains, the target domain, and the optimal transportation losses from each source domain to the target domain, we can estimate the weights of each source domain, and meanwhile, the parameters of the feature extractor and classifier are also updated. All the learnable parameters and the computations of the three modules are updated alternatively. Extensive experimental results on four widely used 2D attack datasets and three recently published 3D attack datasets under both single- and multi-source domain adaptation settings (including both close-set and open-set) show the advantages of our proposed method for cross-scenario face anti-spoofing.