Multi-view exclusive unsupervised dimension reduction for video-based facial expression recognition

Xie, L; Tao, D; Wei, H

Multi-view exclusive unsupervised dimension reduction for video-based facial expression recognition

Xie, L Tao, D

Wei, H

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Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2016, 2016-January pp. 2217 - 2223
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Xie, L	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Wei, H	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2016, 2016-January pp. 2217 - 2223	en_US
dc.identifier.issn	1045-0823	en_US
dc.identifier.uri	http://hdl.handle.net/10453/96017
dc.description.abstract	Video-based facial expression recognition (FER) has recently received increased attention as a result of its widespread application. Many kinds of features have been proposed to represent different properties of facial expressions in videos. However the dimensionality of these features is usually high. In addition, due to the complexity of the information available in video sequences, using only one type of feature is often inadequate. How to effectively reduce the dimensionality and combine multi-view features thus becomes a challenging problem. In this paper, motivated by the recent success in exclusive feature selection, we first introduce exclusive group LASSO (EG-LASSO) to unsupervised dimension reduction (UDR). This leads to the proposed exclusive UDR (EUDR) framework, which allows arbitrary sparse structures on the feature space. To properly combine multiple kinds of features, we further extend EUDR to multi-view EUDR (MEUDR), where the structured sparsity is enforced at both intra- and inter-view levels. In addition, combination weights are learned for all views to allow them to contribute differently to the final consensus presentation. A reliable solution is then obtained. Experiments on two challenging video-based FER datasets demonstrate the effectiveness of the proposed method.	en_US
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence	en_US
dc.title	Multi-view exclusive unsupervised dimension reduction for video-based facial expression recognition	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-January	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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.publication-status	Published	en_US
pubs.volume	2016-January	en_US

Abstract:

Video-based facial expression recognition (FER) has recently received increased attention as a result of its widespread application. Many kinds of features have been proposed to represent different properties of facial expressions in videos. However the dimensionality of these features is usually high. In addition, due to the complexity of the information available in video sequences, using only one type of feature is often inadequate. How to effectively reduce the dimensionality and combine multi-view features thus becomes a challenging problem. In this paper, motivated by the recent success in exclusive feature selection, we first introduce exclusive group LASSO (EG-LASSO) to unsupervised dimension reduction (UDR). This leads to the proposed exclusive UDR (EUDR) framework, which allows arbitrary sparse structures on the feature space. To properly combine multiple kinds of features, we further extend EUDR to multi-view EUDR (MEUDR), where the structured sparsity is enforced at both intra- and inter-view levels. In addition, combination weights are learned for all views to allow them to contribute differently to the final consensus presentation. A reliable solution is then obtained. Experiments on two challenging video-based FER datasets demonstrate the effectiveness of the proposed method.

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http://hdl.handle.net/10453/96017