research-article

Adversarial Learning Domain-Invariant Conditional Features for Robust Face Anti-spoofing

Authors:

Fangling Jiang,

Xiang-Dong Zhou,

Zhenan SunAuthors Info & Claims

International Journal of Computer Vision, Volume 131, Issue 7

Pages 1680 - 1703

https://doi.org/10.1007/s11263-023-01778-x

Published: 28 March 2023 Publication History

Abstract

Face anti-spoofing has been widely exploited in recent years to ensure security in face recognition systems; however, this technology suffers from poor generalization performance on unseen samples. Most previous methods align the marginal distributions from multiple source domains to learn domain-invariant features to mitigate domain shift. However, the category information of samples from different domains is ignored during these marginal distribution alignments; this can potentially lead to features of one category from one domain being misaligned to those of different categories from other domains, although the marginal distributions across domains are well aligned from the whole point of view. In this paper, we propose a simple but effective conditional domain adversarial framework whose main goal is to align the conditional distributions across domains to learn domain-invariant conditional features. Specifically, we first construct a parallel domain structure and its corresponding regularization to reduce negative influences from the finite samples and diversity of spoof face images on the conditional distribution alignments. Then, based on the parallel domain structure, a feature extractor and a global domain classifier, which play a conditional domain adversarial game, are leveraged to make the features of the same category across different domains indistinguishable. Moreover, intra-domain and cross-domain discrimination regularization are further exploited in conjunction with conditional domain adversarial training to minimize the classification error of class predictors. Extensive qualitative and quantitative experiments demonstrate that the proposed method learns well-generalized features from fewer source domains and achieves state-of-the-art performance on six public datasets.

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Cited By

Tian YZhang KHuang YWang LLiu YSun Z(2025)Cross-Optical Property Image Translation for Face Anti-Spoofing: From Visible to PolarizationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.352132320(1192-1205)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TIFS.2024.3521323
Zhong GGuo YYuan JZhang QGuan WChen LCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)PROMOTE: Prior-Guided Diffusion Model with Global-Local Contrastive Learning for Exemplar-Based Image TranslationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680928(3313-3322)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680928
Pei JMen ALiu YZhuang XChen Q(2024)Evidential Multi-Source-Free Unsupervised Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.336197846:8(5288-5305)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TPAMI.2024.3361978
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Published In

cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 131, Issue 7

Jul 2023

265 pages

ISSN:0920-5691

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Kluwer Academic Publishers

United States

Publication History

Published: 28 March 2023

Accepted: 06 March 2023

Received: 30 April 2022

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Scientific Research Foundation of Department of Education of Hunan Province
National Natural Science Foundation of China
National Key Research and Development Program of China

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Cited By

Tian YZhang KHuang YWang LLiu YSun Z(2025)Cross-Optical Property Image Translation for Face Anti-Spoofing: From Visible to PolarizationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.352132320(1192-1205)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TIFS.2024.3521323
Zhong GGuo YYuan JZhang QGuan WChen LCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)PROMOTE: Prior-Guided Diffusion Model with Global-Local Contrastive Learning for Exemplar-Based Image TranslationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680928(3313-3322)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680928
Pei JMen ALiu YZhuang XChen Q(2024)Evidential Multi-Source-Free Unsupervised Domain AdaptationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.336197846:8(5288-5305)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TPAMI.2024.3361978
Jiang FLiu YSi HMeng JLi Q(2024)Cross-Scenario Unknown-Aware Face Anti-Spoofing With Evidential Semantic Consistency LearningIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.335623419(3093-3108)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2024.3356234
Cao JZhang KYao TDing SYang XMa C(2024)Towards Unified Defense for Face Forgery and Spoofing Attacks via Dual Space Reconstruction LearningInternational Journal of Computer Vision10.1007/s11263-024-02151-2132:12(5862-5887)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s11263-024-02151-2
Jiang FLi QWang WRen MShen WLiu BSun Z(2024)Open-Set Single-Domain Generalization for Robust Face Anti-SpoofingInternational Journal of Computer Vision10.1007/s11263-024-02129-0132:11(5151-5172)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s11263-024-02129-0

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