ScleraSegNet: an Improved U-Net Model with Attention for Accurate Sclera Segmentation | |
Wang Caiyong1,2; He Yong2,3; Liu Yunfan2; He Zhaofeng4; He Ran1,2; Sun Zhenan1,2 | |
2020-02 | |
会议名称 | 2019 International Conference on Biometrics (ICB) |
页码 | 1-8 |
会议日期 | 4-7 June 2019 |
会议地点 | Crete, Greece |
摘要 | Accurate sclera segmentation is critical for successful sclera recognition. However, studies on sclera segmentation algorithms are still limited in the literature. In this paper, we propose a novel sclera segmentation method based on the improved U-Net model, named as ScleraSegNet. We perform in-depth analysis regarding the structure of U-Net model, and propose to embed an attention module into the central bottleneck part between the contracting path and the expansive path of U-Net to strengthen the ability of learning discriminative representations. We compare different attention modules and find that channel-wise attention is the most effective in improving the performance of the segmentation network. Besides, we evaluate the effectiveness of data augmentation process in improving the generalization ability of the segmentation network. Experiment results show that the best performing configuration of the proposed method achieves state-of-the-art performance with F-measure values of 91.43%, 89.54% on UBIRIS.v2 and MICHE, respectively. |
DOI | 10.1109/ICB45273.2019.8987270 |
URL | 查看原文 |
收录类别 | EI |
资助项目 | National Key Research and Development Program of China[2017YFC0821602] ; National Natural Science Foundation of China[61573360] ; National Natural Science Foundation of China[61427811] ; National Natural Science Foundation of China[U1836217] ; National Natural Science Foundation of China[61721004] ; National Key Research and Development Program of China[2016YFB1001000] ; National Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[U1836217] ; National Natural Science Foundation of China[61427811] ; National Natural Science Foundation of China[61573360] ; National Key Research and Development Program of China[2017YFC0821602] |
语种 | 英语 |
七大方向——子方向分类 | 机器学习 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39133 |
专题 | 智能感知与计算 |
通讯作者 | Sun Zhenan |
作者单位 | 1.University of Chinese Academy of Sciences 2.Institute of Automation, Chinese Academy of Sciences 3.Hunan University of Technology 4.Beijing IrisKing Co., Ltd |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wang Caiyong,He Yong,Liu Yunfan,et al. ScleraSegNet: an Improved U-Net Model with Attention for Accurate Sclera Segmentation[C],2020:1-8. |
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论文四.pdf(313KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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