Feature level analysis for 3D facial expression recognition

Sha, T; Song, M; Bu, J; Chen, C; Tao, D

Feature level analysis for 3D facial expression recognition

Sha, T Song, M Bu, J Chen, C Tao, D

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2011, 74 (12-13), pp. 2135 - 2141
Issue Date:: 2011-06-01

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Field	Value	Language
dc.contributor.author	Sha, T	en_US
dc.contributor.author	Song, M	en_US
dc.contributor.author	Bu, J	en_US
dc.contributor.author	Chen, C	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2011-06-01	en_US
dc.identifier.citation	Neurocomputing, 2011, 74 (12-13), pp. 2135 - 2141	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15202
dc.description.abstract	3D facial expression recognition has great potential in human computer interaction and intelligent robot systems. In this paper, we propose a two-step approach which combines both the feature selection and the feature fusion techniques to choose more comprehensive and discriminative features for 3D facial expression recognition. In the feature selection stage, we utilize a novel normalized cut-based filter (NCBF) algorithm to select the high relevant and low redundant geometrically localized features (GLF) and surface curvature features (SCF), respectively. Then in the feature fusion stage, PCA is performed on the selected GLF and SCF in order to avoid the curse-of-dimensionality challenge. Finally, the processed GLF and SCF are fused together to capture the most discriminative information in 3D expressional faces. Experiments are carried out on the BU-3DFE database, and the proposed approach outperforms the conventional methods by providing more competitive results. © 2011 Elsevier B.V.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2011.01.008	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Feature level analysis for 3D facial expression recognition	en_US
dc.type	Journal Article
utslib.citation.volume	12-13	en_US
utslib.citation.volume	74	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
dc.location.activity	WOS:000291915500010	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.issue	12-13	en_US
pubs.publication-status	Published	en_US
pubs.volume	74	en_US

Abstract:

3D facial expression recognition has great potential in human computer interaction and intelligent robot systems. In this paper, we propose a two-step approach which combines both the feature selection and the feature fusion techniques to choose more comprehensive and discriminative features for 3D facial expression recognition. In the feature selection stage, we utilize a novel normalized cut-based filter (NCBF) algorithm to select the high relevant and low redundant geometrically localized features (GLF) and surface curvature features (SCF), respectively. Then in the feature fusion stage, PCA is performed on the selected GLF and SCF in order to avoid the curse-of-dimensionality challenge. Finally, the processed GLF and SCF are fused together to capture the most discriminative information in 3D expressional faces. Experiments are carried out on the BU-3DFE database, and the proposed approach outperforms the conventional methods by providing more competitive results. © 2011 Elsevier B.V.

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http://hdl.handle.net/10453/15202