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.
Keywords
MSC
68T10, 68U10, 68T45
Cite this article
@article{cai-110148,
author = {Yongfeng Qi and Jiashu Zhang},
title = {Supervised Kernel Locally Principle Component Analysis for Face Recognition},
journal = {Computing and Informatics},
volume = {31},
number = {6+},
pages = {1465--1479},
year = {2012},
issn = {1335-9150},
url = {https://cai.type.sk/content/2012/6%2B/supervised-kernel-locally-principle-component-analysis-for-face-recognition/}
}
TY - JOUR AU - Yongfeng Qi AU - Jiashu Zhang TI - Supervised Kernel Locally Principle Component Analysis for Face Recognition JO - Computing and Informatics VL - 31 IS - 6+ SP - 1465 EP - 1479 PY - 2012 SN - 1335-9150 UR - https://cai.type.sk/content/2012/6%2B/supervised-kernel-locally-principle-component-analysis-for-face-recognition/ KW - Kernel trick KW - within-class geometric structure KW - principal component analysis KW - face recognition ER -
Qi, Y., & Zhang, J. (2012). Supervised Kernel Locally Principle Component Analysis for Face Recognition. Computing and Informatics, 31(6+), 1465–1479. https://cai.type.sk/content/2012/6%2B/supervised-kernel-locally-principle-component-analysis-for-face-recognition/