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Palmprint Recognition Based on Improved 2DPCA

  • Junwei Tao
  • Wei Jiang
  • Zan Gao
  • Shuang Chen
  • Chao Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4088)

Abstract

Palmprint recognition received many researchers’ attention because of it’s low resolution and cheap devices. As other biometrics, algebraic feature is the prevailing method for palmprint recognition. PCA (principal component analysis) is one prevailing algebraic transformation. It has been a successful feature detection method for pattern recognition. It deals with image vector whose dimension is usually high. 2DPCA is a novel PCA method for image matrix, and it can calculate the covariance matrix more precise. In this paper we apply the new 2DPCA method to palmprint recognition, and we make an improvement in the selection of principal components. In our method we select the principal component that is better for classification. At last we do the improved 2DPCA on the row and column direction to reduce dimension in both direction. Then we apply the method to PolyU Palmprint Database. The experiment result shows that our method got more recognition rate with lower dimensions.

Keywords

Recognition Rate Projection Vector Image Vector Total Scatter Palmprint Image 
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

  • Junwei Tao
    • 1
  • Wei Jiang
    • 1
  • Zan Gao
    • 1
  • Shuang Chen
    • 1
  • Chao Wang
    • 1
  1. 1.School of Information Science & EngineeringShandong UniversityJinan

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