Binary two-dimensional PCA

Pang, Y; Tao, D; Yuan, Y; Li, X

Binary two-dimensional PCA

Pang, Y Tao, D

Yuan, Y Li, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2008, 38 (4), pp. 1176 - 1180
Issue Date:: 2008-08-01

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Field	Value	Language
dc.contributor.author	Pang, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Yuan, Y	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2008-08-01	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2008, 38 (4), pp. 1176 - 1180	en_US
dc.identifier.issn	1083-4419	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15060
dc.description.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. © 2008 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics	en_US
dc.relation.isbasedon	10.1109/TSMCB.2008.923151	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Face	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Image Interpretation, Computer-Assisted	en_US
dc.subject.mesh	Biometry	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Principal Component Analysis	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Binary two-dimensional PCA	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	38	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	38	en_US

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. © 2008 IEEE.

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http://hdl.handle.net/10453/15060