Unsupervised Compound Domain Adaptation for Face Anti-Spoofing

TL;DR

This paper introduces an unsupervised compound domain adaptation method for face anti-spoofing, effectively handling diverse spoof types and environmental variations to improve robustness in real-world verification systems.

Contribution

It is the first to demonstrate the effectiveness of compound domain adaptation for face anti-spoofing, proposing a memory augmentation approach combined with curriculum learning and domain-agnostic training.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Successfully adapts to multiple new spoof types in the target domain.

Enhances robustness of face verification systems in diverse conditions.

Abstract

We address the problem of face anti-spoofing which aims to make the face verification systems robust in the real world settings. The context of detecting live vs. spoofed face images may differ significantly in the target domain, when compared to that of labeled source domain where the model is trained. Such difference may be caused due to new and unknown spoof types, illumination conditions, scene backgrounds, among many others. These varieties of differences make the target a compound domain, thus calling for the problem of the unsupervised compound domain adaptation. We demonstrate the effectiveness of the compound domain assumption for the task of face anti-spoofing, for the first time in this work. To this end, we propose a memory augmentation method for adapting the source model to the target domain in a domain aware manner. The adaptation process is further improved by using the curriculum learning and the domain agnostic source network training approaches. The proposed method successfully adapts to the compound target domain consisting multiple new spoof types. Our experiments on multiple benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-art.