NDLI: Kernel based 2D symmetrical principal component analysis for face classification

Content Provider	IEEE Xplore Digital Library
Author	Cong-De Lu Yu-Lei Chen Bin-Bin He
Copyright Year	2008
Description	Author affiliation: Sch. of Autom. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu (Cong-De Lu)
Abstract	This paper presents a novel algorithm-kernel based 2D symmetrical principal component analysis (K2DSPCA), which takes full advantage of kernel method, the symmetrical property of facial image and the structural information of image (i.e., the advantage of two-dimensional PCA). Firstly, a facial image is decomposed into an even image and an odd image; Secondly, both the even image and the odd image are mapped to a high dimensional feature space (Reproducing Kernel Hilbert space, RKHS) by a nonlinear function; Thirdly, compute the eigenvectors and the eigenvectors of the even image and the odd image in RKHS, respectively; At last, select the eigenvectors with greater variance as the projection axis. We compare the performance of SPCA, 2DPCA, S2DPCA with K2DSPCA on CBCL database for binary classification, and on ORL face database for multi-category classification, respectively. The experimental results show the K2DSPCA is competitive with or superior to SPCA, 2DPCA and S2DPCA.
Starting Page	442
Ending Page	447
File Size	347179
Page Count	6
File Format	PDF
ISBN	9781424420957
DOI	10.1109/ICMLC.2008.4620446
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Kernel Feature extraction Databases Face Training Covariance matrix kernel based two-dimensional symmetrical principal component analysis kernel principal component analysis symmetrical principal component analysis two-dimensional principal component analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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