Unified polarimetric method for cross-domain face attacks detection

Face spoofing detection techniques have performed well in the digital and physical domains separately. However, existing methods do not work well when both types of spoofing attacks need to be resisted at the same time. We propose a new polarization-based unified spoofing detection method for cross-domain face spoofing attacks. With our cross-domain unified spoofing detection framework, our methods can automatically detect and identify face spoofing attacks in both digital and physical domains. In addition, we build a new face anti-spoofing dataset containing polarized modality. We first provide a method for generating polarimetric face images from visible images, which are used to provide a digital domain spoofing attack. Then, we fake faces through physical methods such as photo and mask. In our new dataset, extensive experiments show that our method has better performance and robustness in face cross-domain attack detection and can still defend against cross-domain face attacks with a very small training data size.