EnfoMax：面向域泛化人脸防伪的域熵与互信息最大化
EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing
AI总结：
本文提出 EnfoMax 框架，从信息论角度定义域泛化人脸防伪目标，通过最大化源域活体样本的域熵与互信息，在无对抗学习下提升跨域稳定性与性能。
摘要：
人脸防伪（FAS）方法在域内设置下表现良好，但其跨域性能并不理想。因此，域泛化（DG）方法在 FAS 中受到越来越多关注。现有方法将 FAS 视为简单的二分类任务，并提出启发式训练目标来学习域不变特征。然而，对于什么是域不变特征，目前尚无理论解释。此外，理论支持的缺乏使对抗训练等域泛化技术缺少训练稳定性。为解决这些问题，本文提出 EnfoMax 框架，利用信息论分析跨域 FAS 任务。该框架为域泛化 FAS 任务提供理论保证和优化目标。EnfoMax 在不使用对抗学习的情况下，最大化源域中活体样本的域熵和互信息。实验结果表明，该方法在大量公共数据集上表现良好，并优于最先进方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
