  With the increasing variations of face presentation attacks, model
generalization becomes an essential challenge for a practical face
anti-spoofing system. This paper presents a generalized face anti-spoofing
framework that consists of three tasks: depth estimation, face parsing, and
live/spoof classification. With the pixel-wise supervision from the face
parsing and depth estimation tasks, the regularized features can better
distinguish spoof faces. While simulating domain shift with meta-learning
techniques, the proposed one-side triplet loss can further improve the
generalization capability by a large margin. Extensive experiments on four
public datasets demonstrate that the proposed framework and training strategies
are more effective than previous works for model generalization to unseen
domains.
