CASIA OpenIR  > 多模态人工智能系统全国重点实验室
Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection
Ajian, Liu1; Chenxu, Zhao2; Zitong, Yu3; Jun, Wan1; Anyang, Su2; Xing, Liu2; Zichang, Tan4; Sergio, Escalera5; Junliang, Xing6; Yanyan, Liang7; Guodong, Guo4; Zhen, Lei1; Stan, Z., Li8; Du, Zhang7
Source PublicationIEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
2022
Volume17Pages:2497-2507
Abstract

Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from several drawbacks: 1) a limited number of mask identities, types of sensors, and a total number of videos; 2) low-fidelity quality of facial masks. Basic deep models and remote photoplethysmography (rPPG) methods achieved acceptable performance on these benchmarks but still far from the needs of practical scenarios. To bridge the gap to realworld applications, we introduce a large-scale High-Fidelity Mask dataset, namely HiFiMask. Specifically, a total amount of 54, 600 videos are recorded from 75 subjects with 225 realistic masks by 7 new kinds of sensors. Along with the dataset, we propose a novel Contrastive Context-aware Learning (CCL) framework. CCL is a new training methodology for supervised PAD tasks, which is able to learn by leveraging rich contexts accurately (e.g., subjects, mask material and lighting) among pairs of live faces and high-fidelity mask attacks. Extensive experimental evaluations on HiFiMask and three additional 3D mask datasets demonstrate the effectiveness of our method. The codes and dataset will be released soon.

Indexed BySCIE
Language英语
Sub direction classification模式识别基础
planning direction of the national heavy laboratory视觉信息处理
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57117
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorJun, Wan
Affiliation1.Institute of Automation, Chinese Academy of Sciences, China
2.Mininglamp Technology
3.University of Oulu
4.Baidu Research
5.Universitat de Barcelona
6.Tsinghua University
7.Macau University of Science and Technology
8.Westlake University
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ajian, Liu,Chenxu, Zhao,Zitong, Yu,et al. Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2022,17:2497-2507.
APA Ajian, Liu.,Chenxu, Zhao.,Zitong, Yu.,Jun, Wan.,Anyang, Su.,...&Du, Zhang.(2022).Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,17,2497-2507.
MLA Ajian, Liu,et al."Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 17(2022):2497-2507.
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