Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18906
Title: Binary two-dimensional PCA
Authors: Pang, Y
Tao, D
Yuan, Y
Li, X
Keywords: 2-D PCA (2DPCA)
Face recognition
Haarlike bases
Principal component analysis (PCA)
Subspace selection
Issue Date: 2008
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, 2008, v. 38, no. 4, p. 1176-1180 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics 
Abstract: Fast training and testing procedures are crucial in biometrics recognition research. Conventional algorithms, e.g., principal component analysis (PCA), fail to efficiently work on large-scale and high-resolution image data sets. By incorporating merits from both two-dimensional PCA (2DPCA)-based image decomposition and fast numerical calculations based on Haarlike bases, this technical correspondence first proposes binary 2DPCA (B-2DPCA). Empirical studies demonstrated the advantages of B-2DPCA compared with 2DPCA and binary PCA.
URI: http://hdl.handle.net/10397/18906
ISSN: 1083-4419
DOI: 10.1109/TSMCB.2008.923151
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