An evaluation of bi-modal facial appearance+facial expression face biometrics

Tsai, P; Tran, TP; Hintz, T; Jan, T

An evaluation of bi-modal facial appearance+facial expression face biometrics

Tsai, P Tran, TP Hintz, T Jan, T

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Pattern Recognition, 2008
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Tsai, P	en_US
dc.contributor.author	Tran, TP	en_US
dc.contributor.author	Hintz, T	en_US
dc.contributor.author	Jan, T	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Pattern Recognition, 2008	en_US
dc.identifier.isbn	9781424421756	en_US
dc.identifier.issn	1051-4651	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10936
dc.description.abstract	This paper introduces a framework that employs the Fisher linear discriminant model (FLDM) and classifier (FLDC) on integrated facial appearance and facial expression features. The principal component analysis (PCA) is firstly applied for dimensionality reduction. The normalized fusion method is then applied to the reduced lower dimensional subspaces of these two features. Finally, the FLDM is used for generalizing the most expressive and discriminant feature space for enhancing better generalization performance. Experimental results show that 1) the integrated features of the facial appearance and facial expressions carry the most expressive and discriminant information and 2) the intra-personal variation, indeed, can assist the extra-personal separation. In particular, the proposed method achieves 100% for our database recognition accuracy using only 9 features. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Pattern Recognition	en_US
dc.title	An evaluation of bi-modal facial appearance+facial expression face biometrics	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Tampa, Florida, USA	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 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:

This paper introduces a framework that employs the Fisher linear discriminant model (FLDM) and classifier (FLDC) on integrated facial appearance and facial expression features. The principal component analysis (PCA) is firstly applied for dimensionality reduction. The normalized fusion method is then applied to the reduced lower dimensional subspaces of these two features. Finally, the FLDM is used for generalizing the most expressive and discriminant feature space for enhancing better generalization performance. Experimental results show that 1) the integrated features of the facial appearance and facial expressions carry the most expressive and discriminant information and 2) the intra-personal variation, indeed, can assist the extra-personal separation. In particular, the proposed method achieves 100% for our database recognition accuracy using only 9 features. © 2008 IEEE.

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http://hdl.handle.net/10453/10936