Dynamic Residual Distillation Network for Face Anti-Spoofing With Feature Attention Learning

Abstract

Currently, most face anti-spoofing methods target the generalization problem by relying on auxiliary information such as additional annotations and modalities. However, this auxiliary information is unavailable in practical scenarios, which potentially hinders the application of these methods. Meanwhile, the predetermined or fixed characteristics limit their generalization capability. To countermeasure these problems, a dynamic residual distillation network with feature attention learning (DRDN) is developed to adaptively search discriminative representation and embedding space without accessing any auxiliary information. Specifically, a pixel-level residual distillation module is first designed to obtain domain-irrelevant liveness representation by suppressing both the high-level semantic and low-frequency illumination factors, thus the domain divergence between the source and target domains can be adaptively mitigated. Secondly, a feature-level attention contrastive learning is proposed to construct a distance-aware asymmetrical embedding space to avoid the class boundary over-fitting. Finally, an attention enhancement backbone incorporated with attention blocks is designed for automatically capturing important regions and channels in feature extraction. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art anti-spoofing methods in both single-source and multi-source domain generalization scenarios.