Domain Adversarial Disentanglement Network With Cross-Domain Synthesis for Generalized Face Anti-Spoofing

Abstract

Face Anti-Spoofing (FAS) plays an increasingly important role in face recognition systems for preventing malicious attacks. Existing FAS methods usually show poor generalization performance due to the large difference of domain information (such as collection environment, collection equipment, etc.) between the training set and the testing set. Therefore, we consider training a FAS network that does not pay attention to domain information, in this way to extract relevant liveness feature for the task of FAS. Learned from adversarial learning and disentanglement learning, we design the Domain Adversarial Disentanglement Network with Cross-Domain Synthesis (DADN-CDS) for Face Anti-Spoofing, which achieves disentanglement between domain-irrelevant liveness feature and domain-related feature, so as to minimize the effect of domain information in the needed representation that is used for inference. Specifically, DADN-CDS proposes a dual-branch architecture that can explicitly model and separate out the domain information. To achieve targeted optimization for each component, a novel Task-oriented Three-step Update Strategy (TTUS) is designed to explore a better model update method. Irrelevant loss and 2N-Pair Cross-Domain Loss in TTUS further ensure the disentanglement and task-oriented optimization. Moreover, an Attention-based Cross Synthesis Module (ACSM) is elaborately devised to obtain higher-quality synthetic feature, which performs attention-based feature fusion in a channel-wise way. The design of ACSM can help verify and implicitly facilitate the disentanglement process. Extensive experiments demonstrate that our method achieves the state-of-the-art performance on public datasets and results also suggest the generalization ability of our proposed method.