Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition | |
Wang, Caiyong1,2; Muhammad, Jawad1,2; Wang, Yunlong2; He, Zhaofeng3; Sun, Zhenan1,2 | |
发表期刊 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY |
ISSN | 1556-6013 |
2020 | |
卷号 | 15期号:1页码:2944-2959 |
摘要 | Iris images captured in non-cooperative environments often suffer from adverse noise, which challenges many existing iris segmentation methods. To address this problem, this paper proposes a high-efficiency deep learning based iris segmentation approach, named IrisParseNet. Different from many previous CNN-based iris segmentation methods, which only focus on predicting accurate iris masks by following popular semantic segmentation frameworks, the proposed approach is a complete iris segmentation solution, i.e., iris mask and parameterized inner and outer iris boundaries are jointly achieved by actively modeling them into a unified multi-task network. Moreover, an elaborately designed attention module is incorporated into it to improve the segmentation performance. To train and evaluate the proposed approach, we manually label three representative and challenging iris databases, i.e., CASIA.v4-distance, UBIRIS.v2, and MICHE-I, which involve multiple illumination (NIR, VIS) and imaging sensors (long-range and mobile iris cameras), along with various types of noises. Additionally, several unified evaluation protocols are built for fair comparisons. Extensive experiments are conducted on these newly annotated databases, and results show that the proposed approach achieves state-of-the-art performance on various benchmarks. Further, as a general drop-in replacement, the proposed iris segmentation method can be used for any iris recognition methodology, and would significantly improve the performance of non-cooperative iris recognition. |
关键词 | Iris segmentation iris localization attention mechanism multi-task learning iris recognition |
DOI | 10.1109/TIFS.2020.2980791 |
关键词[WOS] | BIOMETRICS ; IMAGES ; NET |
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[61427811] ; National Key Research and Development Program of China[2017YFC0821602] |
项目资助者 | National Natural Science Foundation of China ; National Key Research and Development Program of China |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000524505300006 |
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/173211/38813 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Sun, Zhenan |
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, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 3.Beijing IrisKing Co Ltd, Beijing 100080, 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 | Wang, Caiyong,Muhammad, Jawad,Wang, Yunlong,et al. Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2020,15(1):2944-2959. |
APA | Wang, Caiyong,Muhammad, Jawad,Wang, Yunlong,He, Zhaofeng,&Sun, Zhenan.(2020).Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,15(1),2944-2959. |
MLA | Wang, Caiyong,et al."Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 15.1(2020):2944-2959. |
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