Generalized face anti-spoofing by detecting pulse from face videos
Face biometric systems are vulnerable to spoofing attacks. Such attacks can be performed in many ways, including presenting a falsified image, video or 3D mask of a valid user. A widely used approach for differentiating genuine faces from fake ones has been to capture their inherent differences in (2D or 3D) texture using local descriptors. One limitation of these methods is that they may fail if an unseen attack type, e.g. a highly realistic 3D mask which resembles real skin texture, is used in spoofing. Here we propose a robust anti-spoofing method by detecting pulse from face videos. Based on the fact that a pulse signal exists in a real living face but not in any mask or print material, the method could be a generalized solution for face liveness detection. The proposed method is evaluated first on a 3D mask spoofing database 3DMAD to demonstrate its effectiveness in detecting 3D mask attacks. More importantly, our cross-database experiment with high quality REAL-F masks shows that the pulse based method is able to detect even the previously unseen mask type whereas texture based methods fail to generalize beyond the development data. Finally, we propose a robust cascade system combining two complementary attack-specific spoof detectors, i.e. utilize pulse detection against print attacks and color texture analysis against video attacks.