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.

References

[1]

Akhtar Z, Micheloni C, and Foresti GL Biometric liveness detection: Challenges and research opportunities IEEE Security and Privacy 2015 13 5 63-72

[2]

Albuquerque, I., Monteiro, J., Darvishi, M., Falk, T. H., & Mitliagkas, I. (2019). Generalizing to unseen domains via distribution matching. arXiv preprint arXiv:1911.00804

[3]

Balaji, Y., Sankaranarayanan, S., & Chellappa, R. (2018). Metareg: Towards domain generalization using meta-regularization. Advances in Neural Information Processing Systems 31.

[4]

Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, and Vaughan JW A theory of learning from different domains Machine Learning 2010 79 1 151-175

[5]

Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., & Hadid, A. (2017). Oulu-npu: A mobile face presentation attack database with real-world variations. In IEEE international conference on automatic face and gesture recognition (pp. 612–618).

[6]

Boulkenafet Z, Komulainen J, and Hadid A Face spoofing detection using colour texture analysis IEEE Transactions on Information Forensics and Security 2016 11 8 1818-1830

[7]

Chen, Z., Yao, T., Sheng, K., Ding, S., Tai, Y., Li, J., Huang, F., & Jin, X. (2021). Generalizable representation learning for mixture domain face anti-spoofing. arXiv preprint arXiv:2105.02453

[8]

Chingovska, I., Anjos, A., & Marcel, S. (2012). On the effectiveness of local binary patterns in face anti-spoofing. In: International conference of biometrics special interest group (pp. 1–7).

[9]

de Freitas Pereira T, Komulainen J, Anjos A, De Martino JM, Hadid A, Pietikäinen M, and Marcel S Face liveness detection using dynamic texture EURASIP Journal on Image and Video Processing 2014 1 2-16

[10]

de Freitas Pereira, T., Anjos, A., De Martino, J. M., & Marcel, S. (2013). Can face anti-spoofing countermeasures work in a real world scenario. In International conference on biometrics (pp. 1–8).

[11]

El-Din, Y. S., Moustafa, M. N., & Mahdi, H. (2021). Adversarial unsupervised domain adaptation guided with deep clustering for face presentation attack detection. arXiv preprint arXiv:2102.06864

[12]

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In IEEE conference on computer vision and pattern recognition (pp. 770–778).

[13]

Heusch G, George A, Geissbühler D, Mostaani Z, and Marcel S Deep models and shortwave infrared information to detect face presentation attacks IEEE Transactions on Biometrics, Behavior, and Identity Science 2020 2 4 399-409

[14]

Hoffman, J., Mohri, M., & Zhang, N. (2018). Algorithms and theory for multiple-source adaptation. In Neural information processing systems (pp. 8256–8266).

[15]

Jia, Y., Zhang, J., Shan, S., & Chen, X. (2020). Single-side domain generalization for face anti-spoofing. In IEEE conference on computer vision and pattern recognition (pp. 8484–8493).

[16]

Jia Y, Zhang J, and Shan S Dual-branch meta-learning network with distribution alignment for face anti-spoofing IEEE Transactions on Information Forensics and Security 2021 17 138-151

[17]

Jia Y, Zhang J, Shan S, and Chen X Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing Pattern Recognition 2021 115

[18]

Li, H., Jialin Pan, S., Wang, S., & Kot, A. C. (2018). Domain generalization with adversarial feature learning. In IEEE conference on computer vision and pattern recognition (pp. 5400–5409).

[19]

Li, Y., Tian, X., Gong, M., Liu, Y., Liu, T., Zhang, K., & Tao, D. (2018). Deep domain generalization via conditional invariant adversarial networks. In European conference on computer vision (pp. 624–639).

[20]

Li, D., Yang, Y., Song, Y.Z., & Hospedales, T.M. (2017). Deeper, broader and artier domain generalization. In IEEE international conference on computer vision (pp. 5542–5550).

[21]

Li H, He P, Wang S, Rocha A, Jiang X, and Kot AC Learning generalized deep feature representation for face anti-spoofing IEEE Transactions on Information Forensics and Security 2018 13 10 2639-2652

[22]

Li H, Li W, Cao H, Wang S, Huang F, and Kot AC Unsupervised domain adaptation for face anti-spoofing IEEE Transactions on Information Forensics and Security 2018 13 7 1794-1809

[23]

Li Y, Li Y, Xu K, Yan Q, and Deng RH Empirical study of face authentication systems under osnfd attacks IEEE Transactions on Dependable and Secure Computing 2016 15 2 231-245

[24]

Liu, Y., Jourabloo, A., & Liu, X. (2018). Learning deep models for face anti-spoofing: Binary or auxiliary supervision. In IEEE conference on computer vision and pattern recognition (pp. 389–398).

[25]

Liu, S., Zhang, K. Y., Yao, T., Bi, M., Ding, S., Li, J., Huang, F., & Ma, L. (2021). Adaptive normalized representation learning for generalizable face anti-spoofing. In Proceedings of the 29th ACM international conference on multimedia (pp. 1469–1477).

[26]

Liu, S., Zhang, K.Y., Yao, T., Sheng, K., Ding, S., Tai, Y., Li, J., Xie, Y., & Ma, L. (2021) Dual reweighting domain generalization for face presentation attack detection. arXiv preprint arXiv:2106.16128

[27]

Li H, Wang S, He P, and Rocha A Face anti-spoofing with deep neural network distillation IEEE Journal of Selected Topics in Signal Processing 2020 14 5 933-946

[28]

Li H, Wang S, Wan R, and Chichung AK Gmfad: Towards generalized visual recognition via multi-layer feature alignment and disentanglement IEEE Transactions on Pattern Analysis and Machine Intelligence 2020 14 5 933-949

[29]

Maaten Lvd and Hinton G Visualizing data using t-sne Journal of Machine Learning Research 2008 9 2579-2605

[30]

Määttä, J., Hadid, A., & Pietikäinen, M. (2011). Face spoofing detection from single images using micro-texture analysis. In IEEE international joint conference on biometrics (pp. 1–7).

[31]

Motiian, S., Piccirilli, M., Adjeroh, D.A., & Doretto, G. (2017). Unified deep supervised domain adaptation and generalization. In IEEE international conference on computer vision (pp. 5715–5725).

[32]

Muandet, K., Balduzzi, D., & Schölkopf, B. (2013). Domain generalization via invariant feature representation. In International conference on machine learning (pp. 10–18).

[33]

Nichol, A., Achiam, J., & Schulman, J. (2018). On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999

[34]

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In IEEE international conference on computer vision (pp 618–626).

[35]

Shao, R., Lan, X., Li, J., & Yuen, P. C. (2019). Multi-adversarial discriminative deep domain generalization for face presentation attack detection. In IEEE conference on computer vision and pattern recognition (pp. 10023–10031).

[36]

Shao R, Lan X, and Yuen PC Regularized fine-grained meta face anti-spoofing Conference on Artificial Intelligence 2020 34 11974-11981

[37]

Tu, X., Zhao, J., Xie, M., Du, G., Zhang, H., Li, J., Ma, Z., & Feng, J. (2019). Learning generalizable and identity-discriminative representations for face anti-spoofing. arXiv preprint arXiv:1901.05602

[38]

Tu X, Zhang H, Xie M, Luo Y, Zhang Y, and Ma Z Deep transfer across domains for face antispoofing Journal of Electronic Imaging 2019 28 4

[39]

Wang, G., Han, H., Shan, S., & Chen, X. (2019). Improving cross-database face presentation attack detection via adversarial domain adaptation. In International conference on biometrics (pp. 1–8).

[40]

Wang, G., Han, H., Shan, S., & Chen, X. (2020). Cross-domain face presentation attack detection via multi-domain disentangled representation learning. In 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR) (pp. 6677–6686).

[41]

Wang, G., Han, H., Shan, S., & Chen, X. (2020). Cross-domain face presentation attack detection via multi-domain disentangled representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6678–6687).

[42]

Wang, J., Lan, C., Liu, C., Ouyang, Y., & Qin, T. (2021). Generalizing to unseen domains: A survey on domain generalization. arXiv preprint arXiv:2103.03097

[43]

Wang, Z., Yu, Z., Wang, X., Qin, Y., Li, J., Zhao, C., Lei, Z., Liu, X., Li, S., & Wang, Z. (2021). Consistency regularization for deep face anti-spoofing. arXiv preprint arXiv:2111.12320

[44]

Wang, J., Zhang, J., Bian, Y., Cai, Y., Wang, C., & Pu, S. (2021). Self-domain adaptation for face anti-spoofing. arXiv preprint arXiv:2102.12129

[45]

Wang, Z., Zhao, C., Qin, Y., Zhou, Q., Qi, G., Wan, J., & Lei, Z. (2018). Exploiting temporal and depth information for multi-frame face anti-spoofing. arXiv preprint arXiv:1811.05118

[46]

Wang G, Han H, Shan S, and Chen X Unsupervised adversarial domain adaptation for cross-domain face presentation attack detection IEEE Transactions on Information Forensics and Security 2020 16 56-69

[47]

Wang Y, Nian F, Li T, Meng Z, and Wang K Robust face anti-spoofing with depth information Journal of Visual Communication and Image Representation 2017 49 332-337

[48]

Wen D, Han H, and Jain AK Face spoof detection with image distortion analysis IEEE Transactions on Information Forensics and Security 2015 10 4 746-761

[49]

Yang, J., Lei, Z., & Li, S. Z. (2014). Learn convolutional neural network for face anti-spoofing. arXiv preprint arXiv:1408.5601

[50]

Yang, J., Lei, Z., Liao, S., & Li, S. Z. (2013). Face liveness detection with component dependent descriptor. In International conference on biometrics (pp. 1–6).

[51]

Yi, D., Lei, Z., Zhang, Z., & Li, S. Z. (2014). Face anti-spoofing: Multi-spectral approach. In Handbook of biometric anti-spoofing (pp. 83–102).

[52]

Yu, Z., Li, X., Shi, J., Xia, Z., & Zhao, G. (2020). Revisiting pixel-wise supervision for face anti-spoofing. arXiv preprint arXiv:2011.12032

[53]

Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., & Li, S. Z. (2012). A face antispoofing database with diverse attacks. In International conference on biometrics (pp. 26–31).

[54]

Zhang K, Zhang Z, Li Z, and Qiao Y Joint face detection and alignment using multitask cascaded convolutional networks IEEE Signal Processing Letters 2016 23 10 1499-1503

[55]

Zhao, H., Des Combes, R. T., Zhang, K., & Gordon, G. (2019). On learning invariant representations for domain adaptation. In International conference on machine learning (pp. 7523–7532).

Cited By

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
Show More Cited By

Recommendations

Open-Set Single-Domain Generalization for Robust Face Anti-Spoofing
Abstract
Face anti-spoofing is a critical component of face recognition technology. However, it suffers from poor generalizability for cross-scenario target domains due to the simultaneous presence of unseen domains and unknown attack types. In this paper, ...
Quality-Invariant Domain Generalization for Face Anti-Spoofing
Abstract
Face Anti-Spoofing (FAS) plays a critical role in safeguarding face recognition systems, while previous FAS methods suffer from poor generalization when applied to unseen domains. Although recent methods have made progress via domain ...
Adversarial learning and decomposition-based domain generalization for face anti-spoofing
Highlights
- A novel framework is proposed to extract domain-invariant features for face anti-spoofing.
Abstract
Face anti-spoofing (FAS) plays a critical role in the face recognition community for securing the face presentation attacks. Many works have been proposed to regard FAS as a domain generalization problem for robust deployment in real-...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

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.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 28 March 2023

Accepted: 06 March 2023

Received: 30 April 2022

Author Tags

Qualifiers

Research-article

Funding Sources

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

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
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

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents