Kernel-based subspace analysis for face recognition

Tsai, P; Jan, T; Hintz, T

Kernel-based subspace analysis for face recognition

Tsai, P Jan, T Hintz, T

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Neural Networks - Conference Proceedings, 2007, pp. 1127 - 1132
Issue Date:: 2007-12-01

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Field	Value	Language
dc.contributor.author	Tsai, P	en_US
dc.contributor.author	Jan, T	en_US
dc.contributor.author	Hintz, T	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	IEEE International Conference on Neural Networks - Conference Proceedings, 2007, pp. 1127 - 1132	en_US
dc.identifier.isbn	142441380X	en_US
dc.identifier.isbn	9781424413805	en_US
dc.identifier.issn	1098-7576	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3170
dc.description.abstract	In face recognition, if the extracted input data contains misleading information (uncertainty), the classifiers may produce degraded classification performance. In this paper, we employed kernel-based discriminant analysis method for the non-separable problems in face recognition under facial expression changes. The effect of the transformations on a subsequent classification was tested in combination with learning algorithms. We found that the transformation of kernel-based discriminant analysis has a beneficial effect on the classification performance. The experimental results indicated that the nonlinear discriminant analysis method dealt with the uncertainty problem very well. Facial expressions can be used as another behavior biometric for human identification. It appears that face recognition may be robust to facial expression changes, and thus applicable. ©2007 IEEE.	en_US
dc.relation.ispartof	IEEE International Conference on Neural Networks - Conference Proceedings	en_US
dc.relation.isbasedon	10.1109/IJCNN.2007.4371116	en_US
dc.title	Kernel-based subspace analysis for face recognition	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080105 Expert Systems	en_US
dc.location.activity	Orlando, USA	en_US
dc.location.activity	Melbourne, Australia
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In face recognition, if the extracted input data contains misleading information (uncertainty), the classifiers may produce degraded classification performance. In this paper, we employed kernel-based discriminant analysis method for the non-separable problems in face recognition under facial expression changes. The effect of the transformations on a subsequent classification was tested in combination with learning algorithms. We found that the transformation of kernel-based discriminant analysis has a beneficial effect on the classification performance. The experimental results indicated that the nonlinear discriminant analysis method dealt with the uncertainty problem very well. Facial expressions can be used as another behavior biometric for human identification. It appears that face recognition may be robust to facial expression changes, and thus applicable. ©2007 IEEE.

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