Knowledge Commons of Institute of Automation,CAS
Contextual Measures for Iris Recognition | |
Wei, Jianze1![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
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ISSN | 1556-6013 |
2023 | |
卷号 | 18页码:57-70 |
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. |
关键词 | Iris recognition contextual aggregation visual transformer information bottleneck |
DOI | 10.1109/TIFS.2022.3221897 |
关键词[WOS] | RANDOMNESS |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | 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) |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000905076700005 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 生物特征识别 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51139 |
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, Inst Microelect, Beijing 100029, 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, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China |
通讯作者单位 | 模式识别国家重点实验室 |
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. |
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