Journal of South China University of Technology (Natural Science Edition) ›› 2011, Vol. 39 ›› Issue (2): 65-70.doi: 10.3969/j.issn.1000-565X.2011.02.011

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Extraction and Recognition of Iris Features Based on Empirical Mode Decomposition and Singular Value Decomposition

Luo Zhong-liang  Lin Tu-sheng  Yang Jun  Zhao Xiao-fang   

  1. South China university of technology, electronic and information institute, guangdong guangzhou 510640
  • Received:2010-05-27 Revised:2010-07-29 Online:2011-02-25 Published:2011-01-02
  • Contact: 罗忠亮(1973-)，男，博士生，主要从事数字图像处理与生物特征识别研究 E-mail:luozl66@yahoo.com.cn
  • About author:罗忠亮(1973-)，男，博士生，主要从事数字图像处理与生物特征识别研究
  • Supported by:

    国家自然科学基金资助项目(60472006，60972136)；广州市科技计划项目(2009J1一C401)

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Abstract:

During the extraction of iris features by the wavelet transform and Gabor filter,the wavelet basis function is fixed and Gabor filter parameters should be optimized.In order to solve these problems,a new extraction method of iris features is proposed based on the empirical mode decomposition（EMD） and the singular value decomposition（SVD）.In this method,first,the preprocessed iris image is decomposed via EMD,and a series of intrinsic mode functions and residual components are obtained to construct the initial feature vector matrixes.Then,the matrixes are decomposed via SVD,and the corresponding singular value is taken as the iris feature vector.Finally,iris features are identified by using a Modest AdaBoost classifier.Experimental results show that the proposed method helps to obtain low-dimension feature vector with higher recognition rate and lower time complexity of feature extraction and matching.

Key words: iris recognition, feature extraction, empirical mode decomposition, singular value decomposition