吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (01): 198-205.

• 论文 • 上一篇    下一篇

基于改进的经验模态分解的虹膜识别方法

李欢利1,2, 郭立红1, 陈涛1, 杨丽梅3, 王心醉4, 董月芳4   

  1. 1. 中国科学院 长春光学精密机械与物理研究所, 长春130033;
    2. 中国科学院 研究生大学, 北京 100039;
    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
  • 收稿日期:2011-11-16 出版日期:2013-01-01 发布日期:2013-01-01
  • 通讯作者: 郭立红(1964-),女,博士,研究员.研究方向:计算机应用.E-mail:goulh@ciomp.ac.cn E-mail:goulh@ciomp.ac.cn
  • 作者简介:李欢利(1986-),女,博士研究生.研究方向:计算机视觉,图像处理.E-mail:lihl483@sina.com
  • 基金资助:

    中国科学院知识创新计划项目(KGCX2-YW-911-2).

Iris recognition based on improved empirical mode decomposition method

LI Huan-li1,2, GUO Li-hong1, CHEN Tao1, YANG Li-mei3, WANG Xin-zui4, DONG Yue-fang4   

  1. 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
  • Received:2011-11-16 Online:2013-01-01 Published:2013-01-01

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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

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.

Key words: computer application, empirical mode decomposition, improved standard deviation, iris recognition

中图分类号: 

  • TP391.4
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