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基于ICA和SVM的虹膜识别方法 被引量：2

Iris Recognition Method Based on ICA and SVM
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摘要 提出了一种基于独立分量分析和支持向量机的虹膜识别方法—ICA提取虹膜特征,SVM实现模式匹配.与G abor小波的方法比较,在编码长度和编码时间方面有较明显地改进.实验结果表明,该算法能够有效地应用到身份鉴别系统中. In this paper, a new iris recognition algorithm based on Independent Component Analysis and Support Vector Machine was proposed. ICA is applied to extract iris feature and SVM is used in pattern matching. Compared with Gabor wavelet method, the size of an iris code and the processing time of the feature extraction are significantly reduced. Experimental results show that the proposed system could be used for a personal identification system in an efficient and effective manner.
作者 李峰 刘国彦 章登勇 朱峰
机构地区 长沙理工大学计算机与通信工程学院
出处 《小型微型计算机系统》 CSCD 北大核心 2005年第12期2203-2206,共4页 Journal of Chinese Computer Systems
基金 湖南省自然科学基金项目(05JJ30123)资助 广东省自然科学基金重点项目(036608)资助 广州市科技计划项目(2003J1-C0201)资助.
关键词 虹膜识别 生物特征识别 独立分量分析 支持向量机 iris recognition biometric independent component analysis support vector machine
分类号 TP319.41 [自动化与计算机技术—计算机软件与理论]
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参考文献11

  • 1Mansfield T, Kelly G, Chandler D. Biometric product testing final report[R]. National Physical Laboratory of UK, 2001,7-12.
  • 2Daugman J. High confidence recognition of person by rapid video analysis of iris texture [C]. IEEE Conf. Publication n408, European convention on security and detection, 16-18May 1995.
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同被引文献14

  • 1Daugman J. High confidence visual recognition of persons by a test of statistical independence [ J ]. IEEE Trans. Pattern Analysis and Machine Intelligence, 1993,15(11) : 1148 - 1161.
  • 2Scholkopf B, Smola A, Muller K R. Nonlinear component analysis as a kernel eigenvalue problem[ J ]. Neural Computation, 1998,10(5) : 1299 - 1319.
  • 3Cao L J, Chua K S, chong W K. A Comparision of PCA, KPCA and ICA for Dimensionality Reduction in Support Vector machine [ J]. Neurocomputing. 2003, 55(2) : 321 -336.
  • 4Daugman J. Biometric Personal Identification System Based on Iris Analysis: US, 5291560[ P]. 1994 -03 -01.
  • 5Yong Wang, Jiuqiang Han. Iris recognition using support vector machines advances [ J ]. Neural Networks, 2004, 8: 622- 628.
  • 6Sholkopf B, Sung K. Comparing support vector machine with Gaussian kernels to radial basis classifiers [ J ]. IEEE Trans. Signal Processing, 1997, 45 ( 11 ) : 2758 - 2765.
  • 7Cortes C, Vapnik V. Support - vector networks [ J]. Machine Learning, 1995, 20:273 -297.
  • 8Vapnik V. An overview of statistical learning theory [J]. IEEE Trans, Neural Networks, 1999, 10:988 - 999.
  • 9Daugman J. High confidence visual recognition of persons by a test of statistical independence[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1993,(11):1148-1161.
  • 10Seholkopf B,Smola A,Muller K R. Nonlinear component analysis as a kernel eigenvalue problem[J].Neural Computation,1998,(05):1299-1319.

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小型微型计算机系统
2005年 第12期

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