Generalized Face Liveness Detection via De-fake Face Generator
Xingming Long, Jie Zhang, Shiguang Shan

TL;DR
This paper introduces a novel face liveness detection method leveraging a large-scale real face dataset and a de-fake face generator to improve generalization to unseen spoofing attacks and domains.
Contribution
It proposes the AG-FAS framework with a de-fake face generator and an off-real attention network, enhancing fake feature learning and cross-domain robustness.
Findings
Achieves state-of-the-art results on nine public datasets.
Effectively generalizes to unseen domains and attack types.
Provides theoretical analysis of anomalous cues.
Abstract
Previous Face Anti-spoofing (FAS) methods face the challenge of generalizing to unseen domains, mainly because most existing FAS datasets are relatively small and lack data diversity. Thanks to the development of face recognition in the past decade, numerous real face images are available publicly, which are however neglected previously by the existing literature. In this paper, we propose an Anomalous cue Guided FAS (AG-FAS) method, which can effectively leverage large-scale additional real faces for improving model generalization via a De-fake Face Generator (DFG). Specifically, by training on a large-scale real face only dataset, the generator obtains the knowledge of what a real face should be like, and thus has the capability of generating a "real" version of any input face image. Consequently, the difference between the input face and the generated "real" face can be treated as…
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Taxonomy
TopicsFace recognition and analysis · Biometric Identification and Security
