The Iris Recognition Based on LMD-PCHIP

LI Huan-li; GUO Li-hong; CHEN Tao; YANG Li-mei; WANG Xin-Zui

doi:10.3788/OPE.20132101.0197

您当前的位置：

首页 >

文章列表页 >

The Iris Recognition Based on LMD-PCHIP

Article | 更新时间：2020-08-12

- The Iris Recognition Based on LMD-PCHIP
- Optics and Precision Engineering Vol. 21, Issue 1, Pages: 197-206(2013)
- 作者机构：
  
  中国科学院长春光学精密机械与物理研究所2. 中国科学院大学3. 长春工业大学4. 中国科学院苏州生物医学工程技术研究所
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132101.0197
  CLC： TP391.4
- Received：15 December 2011，
  
  Revised：02 March 2012，
  
  Published Online：24 January 2013，
  
  Published：15 January 2013
- 稿件说明：
移动端阅览
李欢利郭立红陈涛杨丽梅王心醉. 基于PCHIP-LMD的虹膜识别方法[J]. 光学精密工程, 2013,21(1): 197-206

LI Huan-li GUO Li-hong CHEN Tao YANG Li-mei WANG Xin-zui. The Iris Recognition Based on LMD-PCHIP[J]. Editorial Office of Optics and Precision Engineering, 2013,21(1): 197-206
李欢利郭立红陈涛杨丽梅王心醉. 基于PCHIP-LMD的虹膜识别方法[J]. 光学精密工程, 2013,21(1): 197-206 DOI： 10.3788/OPE.20132101.0197.

LI Huan-li GUO Li-hong CHEN Tao YANG Li-mei WANG Xin-zui. The Iris Recognition Based on LMD-PCHIP[J]. Editorial Office of Optics and Precision Engineering, 2013,21(1): 197-206 DOI： 10.3788/OPE.20132101.0197.

摘要

针对虹膜识别经验模态分解(EMD)和局部均值分解(LMD)方法具有无法兼顾分解速度和包含小误差的缺点，提出了将分段三次Hermite多项式插值引入局部均值分解(PCHIP-LMD)的虹膜识别方法来提高识别准确率。针对虹膜纹理的分布特性，利用PCHIP-LMD对归一化的虹膜图像逐行分解，得到不同尺度的分量图像；通过提取有效的分量图像将其二值化为特征图像。然后用Hamming距离对特征图像进行移位匹配，得到匹配向量。最后计算匹配向量的改进标准差，用此标准差进行虹膜识别。对CASIA1.0、CASIA2.0、CASIA3.0-Interval、MMU1图像库进行了识别试验

结果显示识别率分别达到了99.968 1%、99.884 5%、99.993 7%、99.878 2%。实验结果表明：该方法消除了虹膜特征提取时的高频噪声，有效提取了图像的二值特征，与EMD和LMD方法相比，识别速度，识别准确率和鲁棒性均有极大提高。

Abstract

As Empirical Mode Decomposition (EMD) and Local Mean Decomposition (LMD) cannot take care of both the decomposition rate and the smallest error simultaneously

a fast and effective method combined Piecewise Cubic Hermite Interpolating Polynomial with the LMD(PCHIP-LMD) was proposed to improve the precision of iris recognition. According to the distribution characteristics of iris textures

the PCHIP-LMD method was used to decomposed a normalized recognition image line by line to generate the component image with different scales. Then

the feature image of iris was obtained by binarization of useful components for the iris recognition. Furthermore

the Hamming distance was used to match the feature image by horizontal and vertical shifts to obtain the matched vectors. Finally

the improved standard deviation of the matching vector was calculated and was used to iris recognition. This method was used in CASIA1.0、CASIA2.0、CASIA3.0-Interval and MMU1 database and obtained results show that the correct recognition rates are achieved respectively 99.968 1%

99.884 5%

99.993 5%

99.878 2%. These experimental results demonstrate that the proposed method eliminates the high frequency noise when the iris feature is extracted and obtains the binary feature of the image effectively

which have the advantages of higher speeds

higher recognition rates and better robustness.

关键词

Keywords

references

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)[2]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.[3]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.[4]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.[5]HUANG N, SHEN Z, LONG S, et al.. The empirical mode decomposition and hilbert spectrum for nonlinear and nonstationary time series analysis[J]. Proc. of the Royal Society of London, 1998,454 (1971):903-995.[6]李欣，梅德庆，陈子辰. 基于经验模态分解和希尔伯特-黄变换的精密孔镗削颤振特征提取[J]. 光学精密工程，2011,19(6):1291-1297.LI X, MEI D Q, CHEN Z CH. Feature extraction of chatter for precision hole boring processing based on EMD and HHT[J]. Opt. Precision Eng., 2011,19(6):1291-1297. （in Chinese)[7]SMITH J S. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Inter-face, 2005, 2(5): 443-454.[8]王明达, 张来斌, 梁伟，等. 基于 B样条插值的局部均值分解方法研究[J]. 振动与冲击, 2010, 29(11):73-77. (in Chinese)WANG M D, ZHANG L B, LIANG W, et al.. Local mean decomposition method based on Bpli ne interpolation[J]. Journal of Vibration and Shock, 2010, 29(11) :73-77.[9]程军圣, 张亢, 杨宇，等. 局部均值分解与经验模式分解的对比研究[J]. 振动与冲击, 2009, 28(5) :13- 16.CHENG J SH, ZHANG K, YANG Y, et al.. Comparison between the methods of local mean decomposition and empirical mode decomposition[J]. Journal of Vibration and Shock, 2009, 28(5):13-16. (in Chinese)[10]易大义，陈道崎. 数值分析引论[M]. 杭州:浙江大学出版社，1998.YY D Y, CHEN D Q. Introduction to Numercal Analysis[M], Hangzhou: Zhejiang University press, 1988. (in Chinese)[11]韩民，彭玉华，张顺利，等. 基于经验模态分解的虹膜识别[J]. 光学学报，2010， 30(2):364-368.HAN M, PENG Y H, ZHANG SH L, et al.. Iris Recognition Based on Empirical Mode Decomposition[J]. Acta Optica Sinica, 2010， 30(2):364-368. (in Chinese)[12]刘伟华.基于EMD的虹膜识别方法研究[D].长沙，长沙理工大学，2009.LIU W H. Research of iris recognition based on empirical mode decomposition[D]. Changsha: Changsha University of Science & Technology，2009. (in Chinese)[13]罗忠亮.虹膜生物特征提取与识别[D].广州:华南理工大学，2010.LUO ZH L. Iris biometric extraction and recongnition[D]. Guangzhou: South China University of Technology, 2010. (in Chinese)[14]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，2010,10(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., 2010,10(10):2306-2313. （in Chinese)[15]DAUGMAN J. How iris recognition works[J]. IEEE Transactions on Circuitsand Systems for Video Technology, 2004, 14(1):21-30.[16]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)[17]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)

Views

116

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Iris Recognition Based on SCS-LBP

Realization and improvement of laser ultrasonic signal denoising based on empirical mode decomposition

Iris location algorithm by counting distances

Iris Recognition Based on Zero-crossing Position of Wavelet Transform and Matching Using Variable-length Code

Development and application of Iris recognition technology

Related Author

DONG Yue-Fang

WANG Xin-Zui

LI Xiao-Ming

GUO LI-Hong

LI Huan-Li

PAN Meng-chun

LUO Fei-lu

LUO Shi-tu

Related Institution

中国科学院长春光学精密机械与物理研究所

中国科学院大学

中国航空工业集团公司洛阳电光设备研究所

苏州生物医学工程技术研究所

College of Mechatronics and Automation, National University of Defense Technology

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰