Multi-feature face recognition based on 2D-PCA and SVM

Valuvanathorn, S; Nitsuwat, S; Huang, ML

Multi-feature face recognition based on 2D-PCA and SVM

Valuvanathorn, S Nitsuwat, S Huang, ML

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Publication Type:: Conference Proceeding
Citation:: 2013, 9781461435013 pp. 65 - 75
Issue Date:: 2013-10-01

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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	2013-10-01	en_US
dc.identifier.citation	2013, 9781461435013 pp. 65 - 75	en_US
dc.identifier.isbn	1461435005	en_US
dc.identifier.isbn	9781461435006	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31894
dc.description.abstract	© 2013 Springer Science+Business Media, LLC. All rights reserved. Identification and authentication by face recognition mainly use global face features. However, 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 four parts: the left-eye, right-eye, nose and mouth. This method is based on geometrical techniques used to find location of eyes, nose and mouth from the frontal face image. We used 115 face images for learning and testing. Each-individual person's images are divided into three difference images for training and two difference images for testing. The Two-Dimension Principle Component Analysis (2D-PCA) technique is used for feature extraction and the Support Vector Machine (SVM) method is used for face recognition. The results show that the recognition percentage is 97.83%.	en_US
dc.relation.isbasedon	10.1007/978-1-4614-3501-3_6	en_US
dc.title	Multi-feature face recognition based on 2D-PCA and SVM	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9781461435013	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney
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/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	9781461435013	en_US

Abstract:

© 2013 Springer Science+Business Media, LLC. All rights reserved. Identification and authentication by face recognition mainly use global face features. However, 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 four parts: the left-eye, right-eye, nose and mouth. This method is based on geometrical techniques used to find location of eyes, nose and mouth from the frontal face image. We used 115 face images for learning and testing. Each-individual person's images are divided into three difference images for training and two difference images for testing. The Two-Dimension Principle Component Analysis (2D-PCA) technique is used for feature extraction and the Support Vector Machine (SVM) method is used for face recognition. The results show that the recognition percentage is 97.83%.

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http://hdl.handle.net/10453/31894