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Contextual Measures for Iris Recognition

文献类型：期刊论文

作者Wei, Jianze3; Wang, Yunlong1,2; Huang, Huaibo1,2; He, Ran1,2; Sun, Zhenan1,2; Gao, Xingyu3
刊名IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
出版日期2023
卷号18页码:57-70
ISSN号1556-6013
关键词Iris recognition contextual aggregation visual transformer information bottleneck
DOI10.1109/TIFS.2022.3221897
通讯作者Wang, Yunlong(yunlong.wang@cripac.ia.ac.cn) ; Gao, Xingyu(gxy9910@gmail.com)
英文摘要The iris patterns of the human contain a large amount of randomly distributed and irregularly shaped microstructures. These microstructures make the human iris informative biometric traits. To learn identity representation from them, this paper regards each iris region as a potential microstructure and proposes contextual measures (CM) to model the correlations between them. CM adopts two parallel branches to learn global and local contexts in iris image. The first one is the globally contextual measure branch. It measures the global context involving the relationships between all regions for feature aggregation and is robust to local occlusions. Besides, we improve its spatial perception considering the positional randomness of the microstructures. The other one is the locally contextual measure branch. This branch considers the role of local details in the phenotypic distinctiveness of iris patterns and learns a series of relationship atoms to capture contextual information from a local perspective. In addition, we develop the perturbation bottleneck to make sure that the two branches learn divergent contexts. It introduces perturbation to limit the information flow from input images to identity features, forcing CM to learn discriminative contextual information for iris recognition. Experimental results suggest that global and local contexts are two different clues critical for accurate iris recognition. The superior performance on four benchmark iris datasets demonstrates the effectiveness of the proposed approach in within-database and cross-database scenarios.
WOS关键词RANDOMNESS
资助项目National Natural Science Foundation of China[U1836217] ; National Natural Science Foundation of China[62006225] ; National Natural Science Foundation of China[62006228] ; National Natural Science Foundation of China[62176025] ; National Natural Science Foundation of China[62071468] ; Strategic Priority Research Program of Chinese Academy of Sciences (CAS)[XDA27040700] ; Science and Technology Innovation 2030-Major Project (Brain Science and Brain-Like Intelligence Technology)[2022ZD0208700]
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:000905076700005
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences (CAS) ; Science and Technology Innovation 2030-Major Project (Brain Science and Brain-Like Intelligence Technology)
源URL[http://ir.ia.ac.cn/handle/173211/51138]  
专题多模态人工智能系统全国重点实验室
通讯作者Wang, Yunlong; Gao, Xingyu
作者单位1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp, Inst Automat, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China
推荐引用方式
GB/T 7714		 Wei, Jianze,Wang, Yunlong,Huang, Huaibo,et al. Contextual Measures for Iris Recognition[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2023,18:57-70.
APA Wei, Jianze,Wang, Yunlong,Huang, Huaibo,He, Ran,Sun, Zhenan,&Gao, Xingyu.(2023).Contextual Measures for Iris Recognition.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,18,57-70.
MLA Wei, Jianze,et al."Contextual Measures for Iris Recognition".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 18(2023):57-70.

入库方式： OAI收割

来源：自动化研究所

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