Paper Title:
The Symmetrical Variation of 2DPCA for Face Recognition
  Abstract

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, D. Z. Feng, X. Li, "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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$32.00
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