Supervised Kernel Locally Principle Component Analysis for Face Recognition

keywords: Kernel trick, within-class geometric structure, principal component analysis, face recognition
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.
mathematics subject classification 2000: 68T10, 68U10, 68T45
reference: Vol. 31, 2012, No. 6+, pp. 1465–1479