More Web Proxy on the site http://driver.im/

research-article

Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing

Authors:

Lizhuang MaAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 1469 - 1477

https://doi.org/10.1145/3474085.3475279

Published: 17 October 2021 Publication History

Abstract

With various face presentation attacks arising under unseen scenarios, face anti-spoofing (FAS) based on domain generalization (DG) has drawn growing attention due to its robustness. Most existing methods utilize DG frameworks to align the features to seek a compact and generalized feature space. However, little attention has been paid to the feature extraction process for the FAS task, especially the influence of normalization, which also has a great impact on the generalization of the learned representation. To address this issue, we propose a novel perspective of face anti-spoofing that focuses on the normalization selection in the feature extraction process. Concretely, an Adaptive Normalized Representation Learning (ANRL) framework is devised, which adaptively selects feature normalization methods according to the inputs, aiming to learn domain-agnostic and discriminative representation. Moreover, to facilitate the representation learning, Dual Calibration Constraints are designed, including Inter-Domain Compatible loss and Inter-Class Separable loss, which provide a better optimization direction for generalizable representation. Extensive experiments and visualizations are presented to demonstrate the effectiveness of our method against the SOTA competitors.

References

[1]

Yousef Atoum, Yaojie Liu, Amin Jourabloo, and Xiaoming Liu. 2017. Face anti-spoofing using patch and depth-based CNNs. In International Joint Conference on Biometrics (IJCB).

[2]

Zinelabidine Boulkenafet, Jukka Komulainen, and Abdenour Hadid. 2015. Face anti-spoofing based on color texture analysis. In International Conference on Image Processing (ICIP).

Digital Library

[3]

Zinelabidine Boulkenafet, Jukka Komulainen, and Abdenour Hadid. 2016. Face spoofing detection using colour texture analysis. Transactions on Information Forensics and Security (TIFS).

Digital Library

[4]

Zinelabinde Boulkenafet, Jukka Komulainen, Lei Li, Xiaoyi Feng, and Abdenour Hadid. 2017. Oulu-npu: A mobile face presentation attack database with real-world variations. In International Conference on Automatic Face & Gesture Recognition (FG).

[5]

Zhihong Chen, Taiping Yao, Kekai Sheng, Shouhong Ding, Ying Tai, Jilin Li, Feiyue Huang, and Xinyu Jin. 2021. Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing. In AAAI Conference on Artificial Intelligence (AAAI).

[6]

Ivana Chingovska, André Anjos, and Sébastien Marcel. 2012. On the effectiveness of local binary patterns in face anti-spoofing. In International Conference of Biometrics Special Interest Group (BIOSIG).

[7]

Seokeon Choi, Taekyung Kim, Minki Jeong, Hyoungseob Park, and Changick Kim. 2020. Meta Batch-Instance Normalization for Generalizable Person Re-Identification. arXiv preprint arXiv:2011.14670.

[8]

Tiago de Freitas Pereira, André Anjos, José Mario De Martino, and Sébastien Marcel. 2012. LBP-TOP based countermeasure against face spoofing attacks. In Asian Conference on Computer Vision (ACCV).

Digital Library

[9]

Tiago de Freitas Pereira, Jukka Komulainen, André Anjos, José Mario De Martino, Abdenour Hadid, Matti Pietik"ainen, and Sébastien Marcel. 2014. Face liveness detection using dynamic texture. Journal on Image and Video Processing (JIVP).

[10]

Litong Feng, Lai-Man Po, Yuming Li, Xuyuan Xu, Fang Yuan, Terence Chun-Ho Cheung, and Kwok-Wai Cheung. 2016. Integration of image quality and motion cues for face anti-spoofing: A neural network approach. Journal of Visual Communication and Image Representation (JVCIR).

Digital Library

[11]

Yao Feng, Fan Wu, Xiaohu Shao, Yanfeng Wang, and Xi Zhou. 2018. Joint 3d face reconstruction and dense alignment with position map regression network. In European Conference on Computer Vision (ECCV).

[12]

Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. Conference on Computer Vision and Pattern Recognition (CVPR).

[13]

Jieru Jia, Qiuqi Ruan, and Timothy M Hospedales. 2019. Frustratingly easy person re-identification: Generalizing person re-id in practice. arXiv preprint arXiv:1905.03422.

[14]

Yunpei Jia, Jie Zhang, Shiguang Shan, and Xilin Chen. 2020. Single-Side Domain Generalization for Face Anti-Spoofing. In Conference on Computer Vision and Pattern Recognition (CVPR).

[15]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR).

[16]

Jukka Komulainen, Abdenour Hadid, and Matti Pietikainen. 2013. Context based face anti-spoofing. In International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[17]

Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot. 2018. Domain generalization with adversarial feature learning. In Conference on Computer Vision and Pattern Recognition (CVPR).

[18]

Lei Li, Xiaoyi Feng, Zinelabidine Boulkenafet, Zhaoqiang Xia, Mingming Li, and Abdenour Hadid. 2016. An original face anti-spoofing approach using partial convolutional neural network. In International Conference on Image Processing Theory, Tools and Applications (IPTA).

[19]

Bofan Lin, Xiaobai Li, Zitong Yu, and Guoying Zhao. 2019. Face liveness detection by rppg features and contextual patch-based cnn. In International Conference on Biometric Engineering and Applications (ICBEA).

Digital Library

[20]

Shubao Liu, Ke-Yue Zhang, Taiping Yao, Kekai Sheng, Shouhong Ding, Ying Tai, Jilin Li, Yuan Xie, and Lizhuang Ma. 2021. Dual Reweighting Domain Generalization for Face Presentation Attack Detection. arXiv preprint arXiv:2106.16128.

[21]

Yaojie Liu, Amin Jourabloo, and Xiaoming Liu. 2018. Learning deep models for face anti-spoofing: Binary or auxiliary supervision. In Conference on Computer Vision and Pattern Recognition (CVPR).

[22]

Yaojie Liu, Joel Stehouwer, Amin Jourabloo, and Xiaoming Liu. 2019. Deep tree learning for zero-shot face anti-spoofing. In Conference on Computer Vision and Pattern Recognition (CVPR).

[23]

Yaojie Liu, Joel Stehouwer, and Xiaoming Liu. 2020. On Disentangling Spoof Trace for Generic Face Anti-Spoofing. arXiv preprint arXiv:2007.09273.

[24]

L. V. D. Maaten and Geoffrey E. Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research (JMLR).

[25]

Jukka M"a"att"a, Abdenour Hadid, and Matti Pietikainen. 2011. Face spoofing detection from single images using micro-texture analysis. In International Joint Conference on Biometrics (IJCB).

Digital Library

[26]

Hyeonseob Nam and Hyo-Eun Kim. 2018. Batch-instance normalization for adaptively style-invariant neural networks. Advances in Neural Information Processing Systems (NeurIPS).

Digital Library

[27]

Xuesong Niu, Zitong Yu, Hu Han, Xiaobai Li, Shiguang Shan, and Guoying Zhao. 2020. Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling. In European Conference on Computer Vision (ECCV).

Digital Library

[28]

Xingang Pan, Ping Luo, J. Shi, and X. Tang. 2018. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net. In European Conference on Computer Vision (ECCV).

[29]

Adam Paszke, S. Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, N. Gimelshein, L. Antiga, Alban Desmaison, Andreas Köpf, E. Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Conference and Workshop on Neural Information Processing Systems (NeurIPS).

Digital Library

[30]

Keyurkumar Patel, Hu Han, and Anil K Jain. 2016a. Cross-database face antispoofing with robust feature representation. In Chinese Conference on Biometric Recognition (CCBR).

[31]

Keyurkumar Patel, Hu Han, and Anil K Jain. 2016b. Secure face unlock: Spoof detection on smartphones. Transactions on Information Forensics and Security (TIFS).

Digital Library

[32]

Yunxiao Qin, Chenxu Zhao, Xiangyu Zhu, Zezheng Wang, Zitong Yu, Tianyu Fu, Feng Zhou, Jingping Shi, and Zhen Lei. 2020. Learning meta model for zero-and few-shot face anti-spoofing. In AAAI Conference on Artificial Intelligence (AAAI).

[33]

Rui Shao, Xiangyuan Lan, Jiawei Li, and Pong C Yuen. 2019. Multi-adversarial discriminative deep domain generalization for face presentation attack detection. In Conference on Computer Vision and Pattern Recognition (CVPR).

[34]

Rui Shao, Xiangyuan Lan, and Pong C Yuen. 2020. Regularized Fine-Grained Meta Face Anti-Spoofing. In AAAI Conference on Artificial Intelligence (AAAI).

[35]

Zezheng Wang, Zitong Yu, Chenxu Zhao, Xiangyu Zhu, Yunxiao Qin, Qiusheng Zhou, Feng Zhou, and Zhen Lei. 2020. Deep spatial gradient and temporal depth learning for face anti-spoofing. In Conference on Computer Vision and Pattern Recognition (CVPR).

[36]

Di Wen, Hu Han, and Anil K Jain. 2015. Face spoof detection with image distortion analysis. IEEE Transactions on Information Forensics and Security (TIFS).

[37]

Jianwei Yang, Zhen Lei, and Stan Z Li. 2014. Learn convolutional neural network for face anti-spoofing. arXiv preprint arXiv:1408.5601.

[38]

Jianwei Yang, Zhen Lei, Shengcai Liao, and Stan Z Li. 2013. Face liveness detection with component dependent descriptor. In International Conference on Biometrics (ICB).

[39]

Xiao Yang, Wenhan Luo, Linchao Bao, Yuan Gao, Dihong Gong, Shibao Zheng, Zhifeng Li, and Wei Liu. 2019. Face anti-spoofing: Model matters, so does data. In Conference on Computer Vision and Pattern Recognition (CVPR).

[40]

Zitong Yu, Xiaobai Li, Xuesong Niu, Jingang Shi, and Guoying Zhao. 2020 a. Face anti-spoofing with human material perception. arXiv preprint arXiv:2007.02157.

[41]

Zitong Yu, Xiaobai Li, Jingang Shi, Zhaoqiang Xia, and Guoying Zhao. 2021 a. Revisiting Pixel-Wise Supervision for Face Anti-Spoofing. Transactions on Biometrics, Behavior, and Identity Science (TBIOM).

[42]

Zitong Yu, Wei Peng, Xiaobai Li, Xiaopeng Hong, and Guoying Zhao. 2019. Remote heart rate measurement from highly compressed facial videos: an end-to-end deep learning solution with video enhancement. In International Conference on Computer Vision (ICCV).

[43]

Zitong Yu, Yunxiao Qin, Xiaobai Li, Zezheng Wang, Chenxu Zhao, Zhen Lei, and Guoying Zhao. 2020 b. Multi-Modal Face Anti-Spoofing Based on Central Difference Networks. In Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[44]

Zitong Yu, Yunxiao Qin, Hengshuang Zhao, Xiaobai Li, and Guoying Zhao. 2021 b. Dual-Cross Central Difference Network for Face Anti-Spoofing. In International Joint Conference on Artificial Intelligence (IJCAI).

[45]

Zitong Yu, Chenxu Zhao, Zezheng Wang, Yunxiao Qin, Zhuo Su, Xiaobai Li, Feng Zhou, and Guoying Zhao. 2020 c. Searching central difference convolutional networks for face anti-spoofing. In Conference on Computer Vision and Pattern Recognition (CVPR).

[46]

Jian Zhang, Ying Tai, Taiping Yao, Jia Meng, Shouhong Ding, Chengjie Wang, Jilin Li, Feiyue Huang, and Rongrong Ji. 2021 a. Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System. arXiv preprint arXiv:2102.00713.

[47]

Ke-Yue Zhang, Taiping Yao, Jian Zhang, Shice Liu, Bangjie Yin, Shouhong Ding, and Jilin Li. 2021 b. Structure Destruction and Content Combination for Face Anti-Spoofing. arXiv preprint arXiv:2107.10628.

[48]

Ke-Yue Zhang, Taiping Yao, Jian Zhang, Ying Tai, Shouhong Ding, Jilin Li, Feiyue Huang, Haichuan Song, and Lizhuang Ma. 2020. Face Anti-Spoofing via Disentangled Representation Learning. arXiv preprint arXiv:2008.08250.

[49]

Zhiwei Zhang, Junjie Yan, Sifei Liu, Zhen Lei, Dong Yi, and Stan Z Li. 2012. A face antispoofing database with diverse attacks. In International Conference on Biometrics (ICB).

Cited By

Long XZhang JShan S(2025)Generalized Face Liveness Detection via De-Fake Face GeneratorIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.350710147:3(1818-1831)Online publication date: Mar-2025
https://doi.org/10.1109/TPAMI.2024.3507101
Liu YLi ZWu L(2025)Dual Consistency Regularization for Generalized Face Anti-SpoofingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2025.354065920(2171-2183)Online publication date: 2025
https://doi.org/10.1109/TIFS.2025.3540659
Antil ADhiman C(2025)Unmasking Deception: A Comprehensive Survey on the Evolution of Face Anti‐spoofing MethodsNeurocomputing10.1016/j.neucom.2024.128992617(128992)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128992
Show More Cited By

Index Terms

Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

Recommendations

Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing
MM '22: Proceedings of the 30th ACM International Conference on Multimedia

With various face presentation attacks emerging continually, face anti-spoofing (FAS) approaches based on domain generalization (DG) have drawn growing attention. Existing DG-based FAS approaches always capture the domain-invariant features for ...
Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing
Survey Paper and Regular Paper

Face anti-spoofing aims to detect presentation attack to face recognition--based authentication systems. It has drawn growing attention due to the high security demand. The widely adopted CNN-based methods usually well recognize the spoofing faces when ...
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 ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Shanghai Municipal Science and Technology Major Project
National Key Research and Development Program of China
Zhejiang Lab
National Natural Science Foundation of China

Conference

MM '21

Sponsor:

SIGMM

MM '21: ACM Multimedia Conference

October 20 - 24, 2021

Virtual Event, China

Acceptance Rates

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

66
Total Citations
View Citations
339
Total Downloads

Downloads (Last 12 months)42
Downloads (Last 6 weeks)2

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Long XZhang JShan S(2025)Generalized Face Liveness Detection via De-Fake Face GeneratorIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.350710147:3(1818-1831)Online publication date: Mar-2025
https://doi.org/10.1109/TPAMI.2024.3507101
Liu YLi ZWu L(2025)Dual Consistency Regularization for Generalized Face Anti-SpoofingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2025.354065920(2171-2183)Online publication date: 2025
https://doi.org/10.1109/TIFS.2025.3540659
Antil ADhiman C(2025)Unmasking Deception: A Comprehensive Survey on the Evolution of Face Anti‐spoofing MethodsNeurocomputing10.1016/j.neucom.2024.128992617(128992)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128992
Liu AMa HZheng JYuan HYu XLiang YEscalera SWan JLei ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)FM-CLIP: Flexible Modal CLIP for Face Anti-SpoofingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680856(8228-8237)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680856
Hu XLiu HYuan HFu ZLuo YZhang NZou HGan JZhang YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Fine-Grained Prompt Learning for Face Anti-SpoofingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680855(7619-7628)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680855
Chen CLi YZhang JLiu JWang CGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Generative Data Augmentation with Liveness Information Preserving for Face Anti-SpoofingProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658078(302-310)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658078
Kong ZZhang WLiu FLuo WLiu HShen LRamachandra R(2024)Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-MixingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.324322935:8(10639-10650)Online publication date: Aug-2024
https://doi.org/10.1109/TNNLS.2023.3243229
Zhang DDu ZLi JZhu LShen H(2024)Domain-Adaptive Energy-Based Models for Generalizable Face Anti-SpoofingIEEE Transactions on Multimedia10.1109/TMM.2024.340769726(10474-10488)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3407697
Zheng TLi BWu SWan BMu GLiu SDing SWang J(2024)MFAE: Masked Frequency Autoencoders for Domain Generalization Face Anti-SpoofingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.337126619(4058-4069)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3371266
Fang HLiu AWan JEscalera SZhao CZhang XLi SLei Z(2024)Surveillance Face Anti-SpoofingIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.333797019(1535-1546)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2023.3337970
Show More Cited By

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten