Supervised Kernel Locally Principle Component Analysis for Face Recognition

Year, pages: 2012, 1465 - 1479

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.

Qi, Y., Zhang, J. 2012. Supervised Kernel Locally Principle Component Analysis for Face Recognition. In Computing and Informatics, vol. 31, no.6+, pp. 1465-1479. 1335-9150.

Qi, Y., Zhang, J. (2012). Supervised Kernel Locally Principle Component Analysis for Face Recognition. Computing and Informatics, 31(6+), 1465-1479. 1335-9150.