Palmprint recognition based on unsupervised subspace analysis

  • Authors:
  • Guiyu Feng;Dewen Hu;Ming Li;Zongtan Zhou

  • Affiliations:
  • Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China;Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China;Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China;Department of Automatic Control, College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China

  • Venue:
  • ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
  • Year:
  • 2005

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Abstract

As feature extraction techniques, Kernel Principal Component Analysis (KPCA) and Independent Component Analysis (ICA) can both be considered as generalization of Principal Component Analysis (PCA), which has been used for palmprint recognition and gained satisfactory results [3], therefore it is natural to wonder the performances of KPCA and ICA on this issue. In this paper, palmprint recognition using the KPCA and ICA methods is developed and compared with the PCA method. Based on the experimental results, some useful conclusions are drawn, which fits into the scene for a better picture about considering these unsupervised subspace classifiers for palmprint recognition.