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| 基于改进的经验模态分解的虹膜识别方法 | |
| 引用本文： | 李欢利,郭立红,陈涛,杨丽梅,王心醉,董月芳.基于改进的经验模态分解的虹膜识别方法[J].吉林大学学报(工学版),2013,43(1):198-205. |
| 作者姓名： | 李欢利 郭立红 陈涛 杨丽梅 王心醉 董月芳 |
| 作者单位： | 1. 中国科学院长春光学精密机械与物理研究所,长春130033;中国科学院研究生大学,北京100039 2. 中国科学院长春光学精密机械与物理研究所,长春,130033 3. 长春工业大学机电工程学院,长春,130012 4. 苏州生物医学工程技术研究所,江苏苏州,215163 |
| 基金项目： | 中国科学院知识创新计划项目(KGCX2-YW-911-2) |
| 摘 要： | 首先,采用先行后列的方法对归一化虹膜图像进行经验模态分解,得到不同尺度的固有模态分量;找出有利于识别的分量,将其进行二值化处理生成特征图像;然后对特征图像进行水平和垂直移位匹配,得到海明(Hamming)距离匹配向量,计算匹配向量的改进标准差,以此标准差进行虹膜识别。最后分别对CASIA1、CASIA2、CASIA3-interval、MMU1库进行了识别,结果表明:该方法能够有效地提取图像的二值特征,具有速度快、识别率高等优点。 |
| 关 键 词： | 计算机应用 经验模态分解 改进标准差 虹膜识别 |
Iris recognition based on improved empirical mode decomposition method |
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| LI Huan-li,GUO Li-hong,CHEN Tao,YANG Li-mei,WANG Xin-zui,DONG Yue-fang.Iris recognition based on improved empirical mode decomposition method[J].Journal of Jilin University:Eng and Technol Ed,2013,43(1):198-205. | |
| Authors: | LI Huan-li GUO Li-hong CHEN Tao YANG Li-mei WANG Xin-zui DONG Yue-fang |
| Affiliation: | 1.Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China;2.Graduate University of the Chinese Academy of Sciences,Beijing 100039,China;3.School of Mechatronic Engineering,Changchun University of Technology,Changchun 130012,China;4.Suzhou Institute of Biomedical Engineering and Technology,Suzhou 215163,China) |
| Abstract: | An iris recognition method based on improved empirical mode decomposition is proposed.First,the normalized iris image is decomposed based by row and then column to generate the different layer intrinsic mode components of the image.Second,the feature image is obtained by binarizing the components useful for the iris recognition.Third,the Hamming distance matching vector is obtained by horizontal and vertical shift match.Finally,the improved standard deviation of the matching vector is calculated,which is used as the threshold for iris recognition.This method is tested using CASIA1,CASIA2,CASIA3-Interval and MMU1 databases.Experiment results show that this method can extract the binary feature effectively,with faster speed and higher correct recognition rate. |
| Keywords: | computer application empirical mode decomposition improved standard deviation iris recognition |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |