EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing
This addresses the domain generalization challenge in face anti-spoofing for security applications, offering a theoretically grounded approach to improve cross-domain robustness.
The paper tackled the problem of poor cross-domain performance in face anti-spoofing by proposing the EnfoMax framework, which uses information theory to maximize domain entropy and mutual information for domain generalization, resulting in outperforming state-of-the-art methods on extensive public datasets.
The face anti-spoofing (FAS) method performs well under intra-domain setups. However, its cross-domain performance is unsatisfactory. As a result, the domain generalization (DG) method has gained more attention in FAS. Existing methods treat FAS as a simple binary classification task and propose a heuristic training objective to learn domain-invariant features. However, there is no theoretical explanation of what a domain-invariant feature is. Additionally, the lack of theoretical support makes domain generalization techniques such as adversarial training lack training stability. To address these issues, this paper proposes the EnfoMax framework, which uses information theory to analyze cross-domain FAS tasks. This framework provides theoretical guarantees and optimization objectives for domain-generalized FAS tasks. EnfoMax maximizes the domain entropy and mutual information of live samples in source domains without using adversarial learning. Experimental results demonstrate that our approach performs well on extensive public datasets and outperforms state-of-the-art methods.