  Face Anti-spoofing (FAS) is a challenging problem due to complex serving
scenarios and diverse face presentation attack patterns. Especially when
captured images are low-resolution, blurry, and coming from different domains,
the performance of FAS will degrade significantly. The existing multi-modal FAS
datasets rarely pay attention to the cross-domain problems under deployment
scenarios, which is not conducive to the study of model performance. To solve
these problems, we explore the fine-grained differences between multi-modal
cameras and construct a cross-domain multi-modal FAS dataset under surveillance
scenarios called GREAT-FASD-S. Besides, we propose an Attention based Face
Anti-spoofing network with Feature Augment (AFA) to solve the FAS towards
low-quality face images. It consists of the depthwise separable attention
module (DAM) and the multi-modal based feature augment module (MFAM). Our model
can achieve state-of-the-art performance on the CASIA-SURF dataset and our
proposed GREAT-FASD-S dataset.
