Cascading enhancement representation for face anti-spoofing

Abstract

Face anti-spoofing (FAS) is the first security line of defense in face recognition systems. The majority of current methods focus on distinguishing the live faces from spoof faces by designing an adaptive network with auxiliary information to enhance the feature discrimination, which shows that how to achieve the discriminative representation is also vital to solve FAS task. In this paper, motivated by the idea of cascading enhancement, we propose a novel cascading enhancement representation network (CERN) for effective FAS. Specifically, the CERN utilizes two branches to achieve multi-level feature in cascading enhancement feature extraction stage. The first branch employs the backbone network to concatenate the multi-scale feature in conjunction with attention modules. The second branch utilizes the shared attention modules to enhance the input space for learning the multi-level refinement features. In cascading enhancement feature fusion stage, we transmit the high-level feature to the middle level (mid-level) for enhancing the mid-level representation. The novel mid-level feature is then used to enhance the low-level feature. Moreover, the weight map learning scheme is proposed to further enhance the discrimination of the predicted binary map. Additionally, we use meta-learning to extend our CERN for solving cross-database testing. Experiments on five benchmark databases demonstrate the effectiveness of our methods against the state-of-art methods.