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IriTrack: Face Presentation Attack Detection Using Iris Tracking

Authors:

Liehuang ZhuAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 2

Article No.: 78, Pages 1 - 21

https://doi.org/10.1145/3463515

Published: 24 June 2021 Publication History

Abstract

With a growing adoption of face authentication systems in various application scenarios, face Presentation Attack Detection (PAD) has become of great importance to withstand artefacts. Existing methods of face PAD generally focus on designing intelligent classifiers or customized hardware to differentiate between the image or video samples of a real legitimate user and the imitated ones. Although effective, they can be resource-consuming and suffer from performance degradation due to environmental changes.

In this paper, we propose IriTrack, which is a simple and efficient PAD system that takes iris movement as a significant evidence to identify face artefacts. More concretely, users are required to move their eyes along with a randomly generated poly-line, where the resulting trajectories of their irises are used as an evidence for PAD i.e., a presentation attack will be identified if the deviation of one's actual iris trajectory from the given poly-line exceeds a threshold. The threshold is carefully selected to balance the latency and accuracy of PAD. We have implemented a prototype and conducted extensive experiments to evaluate the performance of the proposed system. The results show that IriTrack can defend against artefacts with moderate time and memory overheads.

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Cited By

He GChen SXu DChen XXie YWang XFang DHui PAmiri Sani ANurmi PLiu Y(2023)Fusang: Graph-inspired Robust and Accurate Object Recognition on Commodity mmWave DevicesProceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services10.1145/3581791.3596849(489-502)Online publication date: 18-Jun-2023
https://doi.org/10.1145/3581791.3596849
Chen YGan HChen HZeng YXu LHeidari AZhu XLiu Y(2023)Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANetNeurocomputing10.1016/j.neucom.2022.10.064517:C(264-278)Online publication date: 14-Jan-2023
https://dl.acm.org/doi/10.1016/j.neucom.2022.10.064
Hernandez-Ortega JFierrez JMorales AGalbally J(2023)Introduction to Presentation Attack Detection in Face Biometrics and Recent AdvancesHandbook of Biometric Anti-Spoofing10.1007/978-981-19-5288-3_9(203-230)Online publication date: 24-Feb-2023
https://doi.org/10.1007/978-981-19-5288-3_9
Show More Cited By

Index Terms

IriTrack: Face Presentation Attack Detection Using Iris Tracking
1. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 5, Issue 2

June 2021

932 pages

EISSN:2474-9567

DOI:10.1145/3472726

Issue’s Table of Contents

Copyright © 2021 ACM.

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Publication History

Published: 24 June 2021

Published in IMWUT Volume 5, Issue 2

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Cited By

He GChen SXu DChen XXie YWang XFang DHui PAmiri Sani ANurmi PLiu Y(2023)Fusang: Graph-inspired Robust and Accurate Object Recognition on Commodity mmWave DevicesProceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services10.1145/3581791.3596849(489-502)Online publication date: 18-Jun-2023
https://doi.org/10.1145/3581791.3596849
Chen YGan HChen HZeng YXu LHeidari AZhu XLiu Y(2023)Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANetNeurocomputing10.1016/j.neucom.2022.10.064517:C(264-278)Online publication date: 14-Jan-2023
https://dl.acm.org/doi/10.1016/j.neucom.2022.10.064
Hernandez-Ortega JFierrez JMorales AGalbally J(2023)Introduction to Presentation Attack Detection in Face Biometrics and Recent AdvancesHandbook of Biometric Anti-Spoofing10.1007/978-981-19-5288-3_9(203-230)Online publication date: 24-Feb-2023
https://doi.org/10.1007/978-981-19-5288-3_9
Haasnoot ESpreeuwers LVeldhuis R(2022)Presentation attack detection and biometric recognition in a challenge-response formalismEURASIP Journal on Information Security10.1186/s13635-022-00131-y2022:1Online publication date: 5-Sep-2022
https://doi.org/10.1186/s13635-022-00131-y
Zhang LLi M(2022)Taekwondo Trajectory Tracking Based on Multitarget Detection AlgorithmMobile Information Systems10.1155/2022/34166822022Online publication date: 13-Sep-2022
https://dl.acm.org/doi/10.1155/2022/3416682
Shen MYu HZhu LXu KLi QHu J(2021)Effective and Robust Physical-World Attacks on Deep Learning Face Recognition SystemsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2021.310249216(4063-4077)Online publication date: 2021
https://doi.org/10.1109/TIFS.2021.3102492

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