Generative Data Augmentation with Liveness Information Preserving for Face Anti-Spoofing

Face anti-spoofing is a critical aspect of ensuring security in the context of human-robot interaction and collaboration. Recently, disentangled-based data augmentation methods have achieved great success in face anti-spoofing tasks. The underlying assumption of those methods is that the liveness information could be completely disentangled and the labeling of the augmented data could totally depend on the liveness-related feature branch. However, we observe that it is almost impossible to extract the liveness-related information completely, which makes the current labeling strategy inaccurate. In this paper, we rethink the disentangling process and propose a novel generative-based data augmentation framework without forcing liveness information encoded into any specific feature space. Specifically, the original images are decomposed into statistic feature space and spatial feature space with liveness information preserving. With these two feature spaces, synthesized liveness-preserving images are generated with the Cartesian product to further approach the distribution of real face anti-spoofing data. Along with the original samplings, the augmented data are fed to a ResNet-based classifier with our proposed pseudo-label strategy for liveness information augmentation. Both qualitative and quantitative experiments demonstrate promising results to show the effectiveness of our proposed method.