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Palmprint Recognition Using ICA Based on Winner-Take-All Network and Radial Basis Probabilistic Neural Network

  • Li Shang
  • De-Shuang Huang
  • Ji-Xiang Du
  • Zhi-Kai Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

This paper proposes a novel method for recognizing palmprint using the winner-take-all (WTA) network based independent component analysis (ICA) algorithm and the radial basis probabilistic neural network (RBPNN) proposed by us. The WTA-ICA algorithm exploits the maximization of the sparse measure criterion as the cost function, and it extracts successfully palmprint features. The classification performance is implemented by the RBPNN. The RBPNN is trained by the orthogonal least square (OLS) algorithm and its structure is optimized by the recursive OLS (ROLS) algorithm. Experimental results show that the RBPNN achieves higher recognition rate and better classification efficiency with other usual classifiers.

Keywords

Recognition Rate Independent Component Analysis Independent Component Analysis High Recognition Rate Independent Component Analysis Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Li Shang
    • 1
    • 2
  • De-Shuang Huang
    • 1
  • Ji-Xiang Du
    • 2
  • Zhi-Kai Huang
    • 2
  1. 1.Department of AutomationUniversity of Science and Technology of ChinaHefei, AnhuiChina
  2. 2.Intelligent Computing Lab, Hefei Institute of Intelligent MachinesChinese Academy of SciencesHefei, AnhuiChina

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