CASIA OpenIR  > 智能感知与计算
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.

DOI10.1109/ICB45273.2019.8987270
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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
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被引频次:17[WOS]   [WOS记录]     [WOS相关记录]
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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