| Iris Localization Based on Edge Searching Strategies |
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| 作者姓名： | 王勇 韩九强 |
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| 作者单位： | School of Electronics and Information Engineering, Xi＇an Jiaotong University, Xi＇an 710049, China |
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| 基金项目： | The National Natural Science Foundation of China （ No. 60174030）. |
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| 摘 要： | An iris localization scheme based on edge searching strategies is presented. First, the edge detection operator Laplacian-of-Gaussian （LOG） is used to iris original image to search its inner boundary. Then, a circle detection operator is introduced to locate the outer boundary and its center, which is invariant of translation, rotation and scale. Finally, the method of curve fitting is developed in localization of eyelid. The performance of the proposed method is tested with 756 iris images from 108 different classes in CASIA Iris Database and compared with the conventional Hough transform method. The experimental results show that without loss of localization accuracy, the proposed iris localization algorithm is apparendy faster than Hough transform.
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| 关 键 词： | 身份智能识别 安全性 信息技术 可靠性 LOG |
| 文章编号： | 1005-2429（2005）02-0119-06 |
| 收稿时间： | 2004-12-13 |
Iris Localization Based on Edge Searching Strategies |
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| Wang Yong,Han Jiuqiang.Iris Localization Based on Edge Searching Strategies[J].Journal of Southwest Jiaotong University,2005,13(2):119-124. |
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| Authors: | Wang Yong Han Jiuqiang |
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| Abstract: | An iris localization scheme based on edge searching strategies is presented. First, the edge detection operator Laplacian-of-Gaussian (LoG) is used to iris original image to search its inner boundary. Then, a circle detection operator is introduced to locate the outer boundary and its center, which is invariant of translation, rotation and scale. Finally, the method of curve fitting is developed in localization of eyelid. The performance of the proposed method is tested with 756 iris images from 108 different classes in CASIA Iris Database and compared with the conventional Hough transform method. The experimental results show that without loss of localization accuracy, the proposed iris localization algorithm is apparently faster than Hough transform. |
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| Keywords: | Iris localization Hough transform Edge detection |
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