A Domain Generalized Face Anti-Spoofing System Using Domain Adversarial Learning

Abstract

This research addresses the enhancement of face anti-spoofing (FAS) in facial recognition systems (FRS) against sophisticated fraudulent activities. Prior methods primarily focus on extracting facial features like color, texture, and dynamic variations, yet these methods struggle to accurately identify common characteristics of forged faces, thereby limiting generalization in practical scenarios. This study aims to propose a novel representation learning framework incorporating adversarial learning algorithms, to segregate features into liveness-specific and domain-specific categories, emphasizing liveness-specific features for FAS advancement. Feature disentanglement is central to this approach, enabling the deep learning models to effectively discern separable latent generating factors, such as identity, liveness, appearance, and texture. This methodology enhances model interpretability, explainability, and generalization. Additionally, Grad-CAM is employed to elucidate the basis for classifications made by the architecture, increasing explainability and trustworthiness. Empirical evaluation across panoply available FAS datasets confirms the superiority, significantly improving performance and robustness over existing technologies.