Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/190
Title: Two-dimensional PCA : a new approach to appearance-based face representation and recognition
Authors: Yang, J
Zhang, DD 
Frangi, AF
Yang, JY
Keywords: Principal component analysis (PCA)
Eigenfaces
Feature extraction
Image representation
Face recognition
Issue Date: Jan-2004
Publisher: IEEE Computer Society
Source: IEEE transactions on pattern analysis and machine intelligence, Jan. 2004, v. 26, no. 1, p. 131-137 How to cite?
Journal: IEEE transactions on pattern analysis and machine intelligence 
Abstract: In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
URI: http://hdl.handle.net/10397/190
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2004.1261097
Rights: © 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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