Regularized Fine-grained Meta Face Anti-spoofing

Understand

Face presentation attacks have become an increasingly critical concern when face recognition is widely applied.

• Many face anti-spoofing methods have been proposed, but most of them ignore the generalization ability to unseen attacks.
• To overcome the limitation, this work casts face anti-spoofing as a domain generalization (DG) problem, and attempts to address this problem by developing a new meta-learning framework called Regularized Fine-grained Meta-learning.
• To let our face anti-spoofing model generalize well to unseen attacks, the proposed framework trains our model to perform well in the simulated domain shift scenarios, which is achieved by finding generalized learning directions in the meta-learning process.