基于统计特征中心对称局部二值模式的虹膜识别

李欢利; 郭立红; 李小明; 王心醉; 董月芳

doi:10.3788/OPE.20132108.2129

您当前的位置：

首页 >

文章列表页 >

基于统计特征中心对称局部二值模式的虹膜识别

信息科学 | 更新时间：2020-08-12

- 基于统计特征中心对称局部二值模式的虹膜识别
- Iris Recognition Based on SCS-LBP
- 光学精密工程 2013年21卷第8期页码：2129-2136
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,130033
  2. 中国航空工业集团公司洛阳电光设备研究所,河南洛阳,471023
  3. 中国科学院大学北京,100039
  4. 苏州生物医学工程技术研究所,江苏苏州,215163
- 作者简介：
- 基金信息：
  
  中科院知识创新项目()
- DOI：10.3788/OPE.20132108.2129
  中图分类号： TP391.4
- 收稿日期：2012-07-17，
  
  修回日期：2012-09-03，
  
  网络出版日期：2013-08-20，
  
  纸质出版日期：2013-08-15
- 稿件说明：
移动端阅览
李欢利郭立红李小明王心醉董月芳. 基于统计特征中心对称局部二值模式的虹膜识别[J]. 光学精密工程, 2013,21(8): 2129-2136

LI Huan-Li GUO LI-Hong LI Xiao-Ming WANG Xin-Zui4 DONG Yue-Fang4. Iris Recognition Based on SCS-LBP[J]. Editorial Office of Optics and Precision Engineering, 2013,21(8): 2129-2136
李欢利郭立红李小明王心醉董月芳. 基于统计特征中心对称局部二值模式的虹膜识别[J]. 光学精密工程, 2013,21(8): 2129-2136 DOI： 10.3788/OPE.20132108.2129.

LI Huan-Li GUO LI-Hong LI Xiao-Ming WANG Xin-Zui4 DONG Yue-Fang4. Iris Recognition Based on SCS-LBP[J]. Editorial Office of Optics and Precision Engineering, 2013,21(8): 2129-2136 DOI： 10.3788/OPE.20132108.2129.

摘要

由于中心对称局部二值模式（CS-LBP）的虹膜识别具有特征维数高、对噪声敏感等缺点，提出了基于统计特征中心对称局部二值模式（SCCS-LBP）的虹膜识别方法。首先，根据虹膜纹理的分布特性，用CS-LBP对归一化的虹膜图像进行编码；为了进一步降低特征维数，对编码后的图像进行特征统计。然后，根据统计结果的分布，提取出有效的二值特征图像。最后，用Hamming距离进行虹膜识别。对CASIA1.0、CASIA2.0、CASIA3.0-Interval、MMU1图像库进行了识别，最高正确识别率分别为99.955%、99.859%、99.989%、99.916%。实验结果表明：该方法有效地利用了虹膜纹理分布特性，与LBP和CS-LBP方法相比，具有更少的特征维数、更高的正确识别率和更好的鲁棒性。

Abstract

As the Center-Symmetric Local Binary Pattern(CS-LBP) for the iris recognition has a higher feature dimension and is sensitive to noises

an effective improved method based on Statistical Characteristics Center-symmetric Local Binary Pattern(SCCS-LBP) was proposed. Firstly

a normalized iris image was encoded by CS-LBP according to the distribution characteristics of iris texture

and the statistical characteristics of the encoded image was computed to reduce the feature dimension. Then

the binary feature image of iris was extracted based on statistical results. Finally

the Hamming distance matching vector was obtained to implement the iris recognition. This method was used to CASIA1.0、CASIA2.0、CASIA3.0-Interval and MMU1 database

the results show that the highest correct recognition rates reach respectively 99.955%

99.859%、99.989%

and 99.916%. The experimental results demonstrate that this method effectively utilizes the iris texture distribution characteristics

and have the advantages of lower dimension

higher recognition rate and better robustness as compared with LBP and CS-LBP methods.

关键词

Keywords

references

沈凌云,郎百和,朱明. 一种基于人工神经网络的人脸识别方法 [J]. 液晶与显示，2011，26(6):836-840.SHEN L Y, LANG B H, ZHU M. Face recognition method based on artificial neural network [J]. Chinese Journal of Liquid Crystals and Displays, 2011，26(6):836-840.(in Chinese)[2]杨云, 岳柱.基于融合图像轮廓矩和Harris角点方法的遮挡人体目标识别研究[J]. 液晶与显示，2013，28(2):273-277.YANG Y, YUE ZH. Human body target recognition under occlusion based on fusion of image contour moment and Harris angular [J]. Chinese Journal of Liquid Crystals and Displays,2013，28(2):273-277.[3]王蕴红，朱勇，谭铁牛. 基于虹膜识别的身份鉴别[J].自动化学报, 2002, 28(1): 1-10.WANG Y H, ZHU Y, TAN T N. Biometrics personal identification based on iris pattern [J]. Acta Automatic Sinica, 2002, 28(1):1-10.(in Chinese)[4]DAUGMAN J. High confidence visual recognition of persons by a test of statistical independence [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993,15 (11):1148-1161.[5]WILDES R,ASMUTH J,GREEN G, et al.. A machine-vision system for iris recognition [J]. Machine Vision and Applications, 1996,9(1):1-8.[6]BOLES W W,BOASHASH B. A human identification technique using images of the iris and wavelet transform [J]. IEEE Transactions on Signal Processing, 1998, 46 (4):1185-1188.[7]OJALA T,VALKEALAHTI K,OJA E,et al..Texture discrimination with multidimensional distributions of signed gray-level differences [J]. Pattern Recognition,2001,34(6):727-739.[8]OJALA T,PIETIKAINEN M,MAENPAA T.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7): 971-987.[9]HEIKKIL M,PIETIKINEN ,SCHMID C. Description of interest regions with local binary patterns [J]. Pattern Recognition, 2009,42( 3) : 425-436.[10]孙哲南.虹膜图像特征表达方法研究[D].北京:中国科学院研究生院，2005.SUN ZH N. A study on iris feature representation[D]. Beijing:Graduate School of Chinese Academy of Sciences, 2005.(in Chinese)[11]程宇奇.用于身份鉴别的虹膜识别方法研究[D].长春:中国科学院长春光学精密机械与物理研究所，2010.CHENG Y Q. Research on iris recognition algorithms for personal identification[D]. Changchun:Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science，2010.(in Chinese)[12]程宇奇,朱明,李桂菊，等. 应用迭代圆环像素率法实现快速虹膜定位[J].光学精密工程，2010,18(10):2306-2313.CHENG Y Q, ZHU M, LI G J, et al.. Rapid iris localization based on method of iterative pixel ratio to cirque area [J]. Opt. Precision Eng., 2010,18(10):2306-2313.(in Chinese)[13]DAUGMAN J. How iris recognition works [J]. IEEE Transactions on Circuitsand Systems for Video Technology,2004,14(1):21-30.[14]何召锋.虹膜图像预处理与特征分析[D].北京:中国科学院自动化研究所，2010.HE ZH F. Iris image preprocessing and feature analysis[D]. Beijing:Institude of Automation, Chinese Academy of Sciences, 2010.(in Chinese)

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于PCHIP-LMD的虹膜识别方法

应用统计距离实现虹膜定位

基于小波过零点位置不等长编码匹配的虹膜识别

虹膜识别技术的发展与应用

基于相位一致性最大响应方向的虹膜识别方法