Multitask deep active contour-based iris segmentation for off-angle iris images | |
Lu, Tianhao1; Wang, Caiyong2; Wang, Yunlong3; Sun, Zhenan3 | |
发表期刊 | JOURNAL OF ELECTRONIC IMAGING |
ISSN | 1017-9909 |
2022-07-01 | |
卷号 | 31期号:4页码:21 |
摘要 | Iris recognition has been considered as a secure and reliable biometric technology. However, iris images are prone to off-angle or are partially occluded when captured with fewer user cooperations. As a consequence, iris recognition especially iris segmentation suffers a serious performance drop. To solve this problem, we propose a multitask deep active contour model for off-angle iris image segmentation. Specifically, the proposed approach combines the coarse and fine localization results. The coarse localization detects the approximate position of the iris area and further initializes the iris contours through a series of robust preprocessing operations. Then, iris contours are represented by 40 ordered isometric sampling polar points and thus their corresponding offset vectors are regressed via a convolutional neural network for multiple times to obtain the precise inner and outer boundaries of the iris. Next, the predicted iris boundary results are regarded as a constraint to limit the segmentation range of noise-free iris mask. Besides, an efficient channel attention module is introduced in the mask prediction to make the network focus on the valid iris region. A differentiable, fast, and efficient SoftPool operation is also used in place of traditional pooling to keep more details for more accurate pixel classification. Finally, the proposed iris segmentation approach is combined with off-the-shelf iris feature extraction models including traditional OM and deep learning-based FeatNet for iris recognition. The experimental results on two NIR datasets CASIA-Iris-off-angle, CASIA-Iris-Africa, and a VIS dataset SBVPI show that the proposed approach achieves a significant performance improvement in the segmentation and recognition for both regular and off-angle iris images. |
关键词 | iris recognition iris segmentation off-angle iris image active contour attention mechanism |
DOI | 10.1117/1.JEI.31.4.041211 |
关键词[WOS] | RECOGNITION ; NETWORK ; NET |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62106015] ; National Natural Science Foundation of China[U1836217] ; National Natural Science Foundation of China[62006225] ; National Natural Science Foundation of China[62071468] ; National Natural Science Foundation of China[61906199] ; Beijing University of Civil Engineering and Architecture Research Capacity Promotion Program for Young Scholars[X21079] |
项目资助者 | National Natural Science Foundation of China ; Beijing University of Civil Engineering and Architecture Research Capacity Promotion Program for Young Scholars |
WOS研究方向 | Engineering ; Optics ; Imaging Science & Photographic Technology |
WOS类目 | Engineering, Electrical & Electronic ; Optics ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000848751400011 |
出版者 | SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS |
是否为代表性论文 | 否 |
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/173211/50021 |
专题 | 模式识别实验室 |
通讯作者 | Wang, Caiyong; Sun, Zhenan |
作者单位 | 1.Hunan Univ Technol, Zhuzhou, Hunan, Peoples R China 2.Beijing Univ Civil Engn & Architecture, Sch Elect & Informat Engn, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing, 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 | Lu, Tianhao,Wang, Caiyong,Wang, Yunlong,et al. Multitask deep active contour-based iris segmentation for off-angle iris images[J]. JOURNAL OF ELECTRONIC IMAGING,2022,31(4):21. |
APA | Lu, Tianhao,Wang, Caiyong,Wang, Yunlong,&Sun, Zhenan.(2022).Multitask deep active contour-based iris segmentation for off-angle iris images.JOURNAL OF ELECTRONIC IMAGING,31(4),21. |
MLA | Lu, Tianhao,et al."Multitask deep active contour-based iris segmentation for off-angle iris images".JOURNAL OF ELECTRONIC IMAGING 31.4(2022):21. |
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2022-【JEI】-Tianhao-M(5346KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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