  Face anti-spoofing (FAS) approaches based on unsupervised domain adaption
(UDA) have drawn growing attention due to promising performances for target
scenarios. Most existing UDA FAS methods typically fit the trained models to
the target domain via aligning the distribution of semantic high-level
features. However, insufficient supervision of unlabeled target domains and
neglect of low-level feature alignment degrade the performances of existing
methods. To address these issues, we propose a novel perspective of UDA FAS
that directly fits the target data to the models, i.e., stylizes the target
data to the source-domain style via image translation, and further feeds the
stylized data into the well-trained source model for classification. The
proposed Generative Domain Adaptation (GDA) framework combines two carefully
designed consistency constraints: 1) Inter-domain neural statistic consistency
guides the generator in narrowing the inter-domain gap. 2) Dual-level semantic
consistency ensures the semantic quality of stylized images. Besides, we
propose intra-domain spectrum mixup to further expand target data distributions
to ensure generalization and reduce the intra-domain gap. Extensive experiments
and visualizations demonstrate the effectiveness of our method against the
state-of-the-art methods.
