Fast haar transform based feature extraction for face representation and recognition

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

Fast haar transform based feature extraction for face representation and recognition

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

Pan, J

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Forensics and Security, 2009, 4 (3), pp. 441 - 450
Issue Date:: 2009-09-01

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Field	Value	Language
dc.contributor.author	Pang, Y	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Yuan, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Pan, J	en_US
dc.date.issued	2009-09-01	en_US
dc.identifier.citation	IEEE Transactions on Information Forensics and Security, 2009, 4 (3), pp. 441 - 450	en_US
dc.identifier.issn	1556-6013	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15229
dc.description.abstract	Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency. © 2006 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Information Forensics and Security	en_US
dc.relation.isbasedon	10.1109/TIFS.2009.2026455	en_US
dc.subject.classification	Strategic, Defence & Security Studies	en_US
dc.title	Fast haar transform based feature extraction for face representation and recognition	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	4	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 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	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	4	en_US

Abstract:

Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency. © 2006 IEEE.

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http://hdl.handle.net/10453/15229