ScleraSegNet: An Attention Assisted U-Net Model for Accurate Sclera Segmentation | |
Wang, Caiyong1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE Transactions on Biometrics, Behavior, and Identity Science
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ISSN | 2637-6407 |
2020-01 | |
卷号 | 2期号:1页码:40-54 |
摘要 | This paper proposes a novel sclera segmentation approach based on an attention assisted U-Net model, named ScleraSegNet. Several off-the-shelf or improved attention modules are incorporated into the central bottleneck part or skip connection part of the original U-Net, helping the new model implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using an external ROI localization model of cascade frameworks. The proposed approach is evaluated on several public, challenging eye datasets and experimental results show that introduced attention modules consistently improve the segmentation performance over the original U-Net across different datasets, and the best performing ScleraSegNet (CBAM) model achieves state-of-theart segmentation performance. By utilizing the high stage's semantic information to guide the selection of features from the low stage in channel-wise and spatial-wise, the further improved ScleraSegNet (SSBC) model ranked first in the Sclera Segmentation Benchmarking Competition 2019 (SSBC 2019), part of ICB 2019, with a Precision value of 92.88% and a Recall value of 90.34% on the MASD.v1 dataset, and a Precision value of 83.19% and a Recall value of 80.01% on the MSD dataset, respectively. |
关键词 | Sclera segmentation sclera recognition U-net attention mechanism SSBC |
学科门类 | 工学 ; 工学::控制科学与工程 |
DOI | 10.1109/TBIOM.2019.2962190 |
URL | 查看原文 |
收录类别 | EI |
语种 | 英语 |
资助项目 | State Key Development Program[2016YFB1001001] ; 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] ; State Key Development Program[2016YFB1001001] |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39127 |
专题 | 模式识别实验室 |
通讯作者 | Sun, Zhenan |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.Beijing IrisKing Company 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,Wang, Yunlong,Liu, Yunfan,et al. ScleraSegNet: An Attention Assisted U-Net Model for Accurate Sclera Segmentation[J]. IEEE Transactions on Biometrics, Behavior, and Identity Science,2020,2(1):40-54. |
APA | Wang, Caiyong,Wang, Yunlong,Liu, Yunfan,He, Zhaofeng,He, Ran,&Sun, Zhenan.(2020).ScleraSegNet: An Attention Assisted U-Net Model for Accurate Sclera Segmentation.IEEE Transactions on Biometrics, Behavior, and Identity Science,2(1),40-54. |
MLA | Wang, Caiyong,et al."ScleraSegNet: An Attention Assisted U-Net Model for Accurate Sclera Segmentation".IEEE Transactions on Biometrics, Behavior, and Identity Science 2.1(2020):40-54. |
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