The Symmetrical Variation of 2DPCA for Face Recognition

Abstract:

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This paper first discusses the relationship of Principal Component Analysis (PCA) and two-dimensional PCA (2DPCA). For 2DPCA eliminating the some covariance information which can be useful for recognition, The symmetrical Variation of 2DPCA for Face recognition (V2DPCA) is proposed. These experiments on both of ORL face bases shows improvement in recognition accuracy, fewer coefficients and recognition time over 2DPCA, and this algorithm is also superior to the traditional eigenfaces, ICA and Kernel eigenfaces in terms of the recognition accuracy.

Info:

Periodical:

Advanced Materials Research (Volumes 255-260)

Edited by:

Jingying Zhao

Pages:

2004-2008

DOI:

10.4028/www.scientific.net/AMR.255-260.2004

Citation:

Z. Yue et al., "The Symmetrical Variation of 2DPCA for Face Recognition", Advanced Materials Research, Vols. 255-260, pp. 2004-2008, 2011

Online since:

May 2011

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Price:

$35.00

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