Surveillance Face Anti-Spoofing
文献类型：期刊论文
| 作者 | Fang, Hao1,2; Liu, Ajian1,2; Wan, Jun1,2,3 ; Escalera, Sergio4,5,6; Zhao, Chenxu7; Zhang, Xu8; Li, Stan Z.3,9; Lei, Zhen1,2,10
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| 刊名 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
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| 出版日期 | 2024 |
| 卷号 | 19页码:1535-1546 |
| ISSN号 | 1556-6013 |
| 关键词 | Face anti-spoofing dataset surveillance scenes |
| DOI | 10.1109/TIFS.2023.3337970 |
| 通讯作者 | Wan, Jun(jun.wan@ia.ac.cn) |
| 英文摘要 | Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL. |
| WOS关键词 | PRESENTATION ATTACK ; RECOGNITION ; DATASET |
| 资助项目 | National Key Research and Development Plan |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001128415900005 |
| 资助机构 | National Key Research and Development Plan |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54892]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Wan, Jun |
| 作者单位 | 1.Chinese Acad Sci CASIA, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Macau Univ Sci & Technol, Fac Innovat Engn, Sch Comp Sci & Engn, Taipa, Macau, Peoples R China 4.Univ Barcelona UB, Dept Math & Informat, Barcelona 08007, Spain 5.Comp Vis Ctr CVC, Barcelona 08193, Spain 6.Aalborg Univ, Visual Anal & Percept VAP Lab, DK-9220 Aalborg, Denmark 7.Mininglamp Technol, Shanghai 200232, Peoples R China 8.Beijing Normal Univ, Sch Artificial Intelligence, Beijing 100875, Peoples R China 9.Westlake Univ, Sch Engn, Hangzhou 310024, Peoples R China 10.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Hao,Liu, Ajian,Wan, Jun,et al. Surveillance Face Anti-Spoofing[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2024,19:1535-1546. |
| APA | Fang, Hao.,Liu, Ajian.,Wan, Jun.,Escalera, Sergio.,Zhao, Chenxu.,...&Lei, Zhen.(2024).Surveillance Face Anti-Spoofing.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,19,1535-1546. |
| MLA | Fang, Hao,et al."Surveillance Face Anti-Spoofing".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 19(2024):1535-1546. |
入库方式： OAI收割
来源：自动化研究所
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