Causal Intervention for Generalizable Face Anti-Spoofing

Abstract

Generalizable face anti-spoofing (FAS) has drawn growing attention due to its robustness to unseen real scenarios. Existing domain generalization methods leverage adversarial learning or meta-learning to mitigate the domain bias and improve generalizability. However, these methods are heuristic and suffer from complicated min-max problems or cumbersome meta-updates. In this paper, we propose a simple yet effective Causal Intervention method for generalizable Face Anti-Spoofing, namely CIFAS. Firstly, we figure out the generalizability is undermined by a domain-aware confounder based on the structural causal model. Instantiating the confounder as the domain-specific factor, a domain embedding module is employed with Dirichlet mixup to obtain representative domain features. Consequently, we propose a novel backdoor adjustment model for causal intervention to capture the true causality and learn a robust FAS model. Our CIFAS is the first attempt to introduce causal learning into FAS. Extensive experiments on seven cross-dataset tests demonstrate that CIFAS outperforms the state-of-the-art methods.