Supervised Kernel Locally Principle Component Analysis for Face Recognition

Authors

  • Yongfeng Qi College of Computer Science and Engineering, Northwest Normal University, Lanzhou
  • Jiashu Zhang Sichuan Province KeyLab of Signal and Information Processing, Southwest Jiaotong University, Chengdu

Keywords:

Kernel trick, within-class geometric structure, principal component analysis, face recognition

Abstract

In this paper, a novel algorithm for feature extraction, named supervised kernel locally principle component analysis (SKLPCA), is proposed. The SKLPCA is a non-linear and supervised subspace learning method, which maps the data into a potentially much higher dimension feature space by kernel trick and preserves the geometric structure of data according to prior class-label information. SKLPCA can discover the nonlinear structure of face images and enhance local within-class relations. Experimental results on ORL, Yale, CAS-PEAL and CMU PIE databases demonstrate that SKLPCA outperforms EigenFaces, LPCA and KPCA.

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Published

2013-01-30

How to Cite

Qi, Y., & Zhang, J. (2013). Supervised Kernel Locally Principle Component Analysis for Face Recognition. Computing and Informatics, 31(6+), 1465–1479. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/1327