Towards More Discriminative and Robust Iris Recognition by Learning Uncertain Factors | |
Wei, Jianze1,2; Huang, Huaibo2,3; Wang, Yunlong2,3; He, Ran2,3; Sun, Zhenan2,3 | |
发表期刊 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY |
ISSN | 1556-6013 |
2022 | |
卷号 | 17页码:865-879 |
摘要 | The uncontrollable acquisition process limits the performance of iris recognition. In the acquisition process, various inevitable factors, including eyes, devices, and environment, hinder the iris recognition system from learning a discriminative identity representation. This leads to severe performance degradation. In this paper, we explore uncertain acquisition factors and propose uncertainty embedding (UE) and uncertainty-guided curriculum learning (UGCL) to mitigate the influence of acquisition factors. UE represents an iris image using a probabilistic distribution rather than a deterministic point (binary template or feature vector) that is widely adopted in iris recognition methods. Specifically, UE learns identity and uncertainty features from the input image, and encodes them as two independent components of the distribution, mean and variance. Based on this representation, an input image can be regarded as an instantiated feature sampled from the UE, and we can also generate various virtual features through sampling. UGCL is constructed by imitating the progressive learning process of newborns. Particularly, it selects virtual features to train the model in an easy-to-hard order at different training stages according to their uncertainty. In addition, an instance-level enhancement method is developed by utilizing local and global statistics to mitigate the data uncertainty from image noise and acquisition conditions in the pixel-level space. The experimental results on six benchmark iris datasets verify the effectiveness and generalization ability of the proposed method on same-sensor and cross-sensor recognition. |
关键词 | Iris recognition Uncertainty Training Task analysis Computational modeling Feature extraction Probabilistic logic Iris recognition cross-sensor recognition uncertainty learning curriculum learning |
DOI | 10.1109/TIFS.2022.3154240 |
关键词[WOS] | SEGMENTATION |
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[U21B2045] ; National Natural Science Foundation of China[62176025] ; National Natural Science Foundation of China[61622310] ; National Natural Science Foundation of China[62071468] ; Beijing Natural Science Foundation[JQ18017] ; Strategic Priority Research Program of Chinese Academy of Sciences (CAS)[XDA27040700] ; Youth Innovation Promotion Association CAS[2015109] ; Chinese Association for Artificial Intelligence (CAAI)-Huawei MindSpore Open Fund |
项目资助者 | National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences (CAS) ; Youth Innovation Promotion Association CAS ; Chinese Association for Artificial Intelligence (CAAI)-Huawei MindSpore Open Fund |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000769991900002 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 生物特征识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48106 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Wang, Yunlong |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
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,Huang, Huaibo,Wang, Yunlong,et al. Towards More Discriminative and Robust Iris Recognition by Learning Uncertain Factors[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2022,17:865-879. |
APA | Wei, Jianze,Huang, Huaibo,Wang, Yunlong,He, Ran,&Sun, Zhenan.(2022).Towards More Discriminative and Robust Iris Recognition by Learning Uncertain Factors.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,17,865-879. |
MLA | Wei, Jianze,et al."Towards More Discriminative and Robust Iris Recognition by Learning Uncertain Factors".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 17(2022):865-879. |
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