Supervised Kernel Locally Principle Component Analysis for Face Recognition
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
Computing and Informatics, Tome 31 (2013) no. 6, / Harvested from Computing and Informatics
Text on Computing and Informatics
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.
Publié le : 2013-01-30
Classification:  Kernel trick, within-class geometric structure, principal component analysis, face recognition,  68T10, 68U10, 68T45 
@article{cai1327,
     author = {Yongfeng Qi; College of Computer Science and Engineering, Northwest Normal University, Lanzhou and Jiashu Zhang; Sichuan Province KeyLab of Signal and Information Processing, Southwest Jiaotong University, Chengdu},
     title = {Supervised Kernel Locally Principle Component Analysis for Face Recognition},
     journal = {Computing and Informatics},
     volume = {31},
     number = {6},
     year = {2013},
     language = {en},
     url = {http://dml.mathdoc.fr/item/cai1327}
}

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. Supervised Kernel Locally Principle Component Analysis for Face Recognition. Computing and Informatics, Tome 31 (2013) no. 6, . http://gdmltest.u-ga.fr/item/cai1327/