Supervised Kernel Locally Principle Component Analysis for Face Recognition
Keywords:
Kernel trick, within-class geometric structure, principal component analysis, face recognitionAbstract
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.Downloads
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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
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Articles