Multi-feature face recognition based on PSO-SVM

Valuvanathorn, S; Nitsuwat, S; Huang, ML

Multi-feature face recognition based on PSO-SVM

Valuvanathorn, S Nitsuwat, S Huang, ML

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Publication Type:: Conference Proceeding
Citation:: International Conference on ICT and Knowledge Engineering, 2012, pp. 140 - 145
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Valuvanathorn, S	en_US
dc.contributor.author	Nitsuwat, S	en_US
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	International Conference on ICT and Knowledge Engineering, 2012, pp. 140 - 145	en_US
dc.identifier.isbn	9781467323147	en_US
dc.identifier.issn	2157-0981	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22967
dc.description.abstract	Face recognition is a kind of identification and authentication, which mainly use the global-face feature. Nevertheless, the recognition accuracy rate is still not high enough. This research aims to develop a method to increase the efficiency of recognition using global-face feature and local-face feature with 4 parts: the left-eye, right-eye, nose and mouth. We used 115 face images from BioID face dataset for learning and testing. Each-individual person's images are divided into 3 different images for training and 2 different images for testing. The processed histogram based (PHB), principal component analysis (PCA) and two-dimension principal component analysis (2D-PCA) techniques are used for feature extraction. In the recognition process, we used the support vector machine (SVM) for classification combined with particle swarm optimization (PSO) to select the parameters G and C automatically (PSO-SVM). The results show that the proposed method could increase the recognition accuracy rate. © 2012 IEEE.	en_US
dc.relation.ispartof	International Conference on ICT and Knowledge Engineering	en_US
dc.relation.isbasedon	10.1109/ICTKE.2012.6408543	en_US
dc.title	Multi-feature face recognition based on PSO-SVM	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Bangkok, Thailand	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Face recognition is a kind of identification and authentication, which mainly use the global-face feature. Nevertheless, the recognition accuracy rate is still not high enough. This research aims to develop a method to increase the efficiency of recognition using global-face feature and local-face feature with 4 parts: the left-eye, right-eye, nose and mouth. We used 115 face images from BioID face dataset for learning and testing. Each-individual person's images are divided into 3 different images for training and 2 different images for testing. The processed histogram based (PHB), principal component analysis (PCA) and two-dimension principal component analysis (2D-PCA) techniques are used for feature extraction. In the recognition process, we used the support vector machine (SVM) for classification combined with particle swarm optimization (PSO) to select the parameters G and C automatically (PSO-SVM). The results show that the proposed method could increase the recognition accuracy rate. © 2012 IEEE.

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http://hdl.handle.net/10453/22967