Abstract
Face anti-spoofing (FAS) aims to strengthen security of facial identity authentication by distinguishing live faces from spoof ones. Although disentangled feature learning has achieved much success in FAS, the representation capacity of disentangled feature space remains limited and does not extend beyond the training domains. In this paper, we propose to further augment the disentangled liveness and domain features with a two-fold goal. Our first goal is to enrich the diversity of liveness features so as to encompass a wide range of facial representation attacks. The second goal is to expand the domain features toward well-generalized and unseen domains. To reach the two goals, we develop a Disentangled Feature Augmentation Network (DFANet) with two feature augmentation strategies, including Affine Feature Transformation (AFT) and Adversarial Domain Learning (ADL). Extensive experiments on four FAS benchmark datasets show that the proposed DFANet outperforms previous methods on most of the protocols under cross-domain testings. The codes are available at https://github.com/Jxchong1999/DFANet.