Face anti-spoofing using feature distilling and global attention learning

Abstract

Face anti-spoofing (FAS) is essential to assure the security of face recognition systems. Recently, some deep learning based FAS methods have achieved promising results under intra-dataset testing. However, they often fail in generalizing to unseen attacks due to the failure of extracting intrinsic features from face images. In this paper, we propose an end-to-end FAS method which consists of an anti-interference feature distillation module, a global spatial attention learning module and a pyramid binary mask supervision module. The deep features from the pretrained ResNet34 network are first distilled at multiple levels to capture intrinsic information via removing interference of features. Then, the multi-level distilled features are further refined by using a global spatial learning mechanism. Finally, the pyramid pixel-wise supervision is assembled to boost performance. Extensive experimental results on five benchmark datasets show the superior performance of our proposed method on intra-dataset testing and on cross-dataset testing.