Face Anti-Spoofing : Detection, Generalization, and Visualization

Face anti-spoofing is the process of distinguishing genuine faces and face presentation attacks: attackers presenting spoofing faces (e.g. photograph, digital screen, and mask) to the face recognition system and attempting to be authenticated as the genuine user. In recent years, face anti-spoofing has brought increasing attention to the vision community as it is a crucial step to prevent face recognition systems from a security breach. Previous approaches formulate face anti-spoofing as a binary classification problem, and many of them struggle to generalize to different conditions(such as pose, lighting, expressions, camera sensors, and unknown spoof types). Moreover, those methods work as a black box and cannot provide interpretation or visualization to their decision. To address those challenges, we investigate face anti-spoofing in 3 stages: detection, generalization and visualization. In the detection stage, we learn a CNN-RNN model to estimate auxiliary tasks of face depth and rPPG signals estimation, which can bring additional knowledge for the spoof detection. In the generalization stage, we investigate the detection of unknown spoof attacks and propose a novel Deep Tree Network (DTN) to well represent the unknown spoof attacks. In the visualization stage, we find “spoof trace, the subtle image pattern in spoof faces (e.g., color distortion, 3D mask edge, and Moire pattern), is effective to explain why a spoof is a spoof. We provide a proper physical modeling of the spoof traces and design a generative model to disentangle the spoof traces from input faces. In addition, we also show that a proper physical modeling can benefit other face problems, such as face shadow detection and removal. A proper shadow modeling can not only detect the shadow region effectively, but also remove the shadow in a visually plausible manner.