Face recognition with adaptive local hyperplane algorithm

Yang, T; Kecman, V

Face recognition with adaptive local hyperplane algorithm

Yang, T Kecman, V

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Pattern Analysis and Applications, 2010, 13 (1), pp. 79 - 83
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Yang, T	en_US
dc.contributor.author	Kecman, V	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Pattern Analysis and Applications, 2010, 13 (1), pp. 79 - 83	en_US
dc.identifier.issn	1433-7541	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13457
dc.description.abstract	The paper introduces a novel adaptive local hyperplane (ALH) classifier and it shows its superior performance in the face recognition tasks. Four different feature extraction methods (2DPCA, (2D)2PCA, 2DLDA and (2D)2LDA) have been used in combination with five classifiers (K-nearest neighbor (KNN), support vector machine (SVM), nearest feature line (NFL), nearest neighbor line (NNL) and ALH). All the classifiers and feature extraction methods have been applied to the renown benchmarking face databasesthe Cambridge ORL database and the Yale database and the ALH classifier with a LDA based extractor outperforms all the other methods on them. The ALH algorithm on these two databases is very promising but more study on larger databases need yet to be done to show all the advantages of the proposed algorithm.	en_US
dc.publisher	Springer	en_US
dc.relation.ispartof	Pattern Analysis and Applications	en_US
dc.relation.isbasedon	10.1007/s10044-008-0138-6	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Face recognition with adaptive local hyperplane algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	13	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0802 Computation Theory and 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.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.volume	13	en_US

Abstract:

The paper introduces a novel adaptive local hyperplane (ALH) classifier and it shows its superior performance in the face recognition tasks. Four different feature extraction methods (2DPCA, (2D)2PCA, 2DLDA and (2D)2LDA) have been used in combination with five classifiers (K-nearest neighbor (KNN), support vector machine (SVM), nearest feature line (NFL), nearest neighbor line (NNL) and ALH). All the classifiers and feature extraction methods have been applied to the renown benchmarking face databasesthe Cambridge ORL database and the Yale database and the ALH classifier with a LDA based extractor outperforms all the other methods on them. The ALH algorithm on these two databases is very promising but more study on larger databases need yet to be done to show all the advantages of the proposed algorithm.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/13457