Regularized Fine-grained Meta Face Anti-spoofing

TL;DR

This paper introduces a novel meta-learning framework with regularization and fine-grained strategies to improve face anti-spoofing models' ability to generalize to unseen attacks, addressing a key limitation in existing methods.

Contribution

It proposes a regularized, fine-grained meta-learning approach that incorporates domain knowledge to enhance generalization in face anti-spoofing under unseen attack scenarios.

Findings

Outperforms existing methods on four public datasets

Improves generalization to unseen attacks

Effective in diverse domain shift scenarios

Abstract

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. Specifically, the proposed framework incorporates the domain knowledge of face anti-spoofing as the regularization so that meta-learning is conducted in the feature space regularized by the supervision of domain knowledge. This enables our model more likely to find generalized learning directions with the regularized meta-learning for face anti-spoofing task. Besides, to further enhance the generalization ability of our model, the proposed framework adopts a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios in each iteration. Extensive experiments on four public datasets validate the effectiveness of the proposed method.