A multi-task model for simultaneous face identification and facial expression recognition

Zheng, H; Geng, X; Tao, D; Jin, Z

A multi-task model for simultaneous face identification and facial expression recognition

Zheng, H Geng, X Tao, D

Jin, Z

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2016, 171 pp. 515 - 523
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Zheng, H	en_US
dc.contributor.author	Geng, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Jin, Z	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Neurocomputing, 2016, 171 pp. 515 - 523	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122660
dc.description.abstract	© 2015 Elsevier B.V. Regarded as two independent tasks, both face identification and facial expression recognition perform poorly given small size training sets. To address this problem, we propose a multi-task facial inference model (MT-FIM) for simultaneous face identification and facial expression recognition. In particular, face identification and facial expression recognition are learnt simultaneously by extracting and utilizing appropriate shared information across them in the framework of multi-task learning, in which the shared information refers to the parameter controlling the sparsity. MT-FIM simultaneously minimizes the within-class scatter and maximizes the distance between different classes to enable the robust performance of each individual task. We conduct comprehensive experiments on three face image databases. The experimental results show that our algorithm outperforms the state-of-the-art algorithms.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2015.06.079	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A multi-task model for simultaneous face identification and facial expression recognition	en_US
dc.type	Journal Article
utslib.citation.volume	171	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
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	171	en_US

Abstract:

© 2015 Elsevier B.V. Regarded as two independent tasks, both face identification and facial expression recognition perform poorly given small size training sets. To address this problem, we propose a multi-task facial inference model (MT-FIM) for simultaneous face identification and facial expression recognition. In particular, face identification and facial expression recognition are learnt simultaneously by extracting and utilizing appropriate shared information across them in the framework of multi-task learning, in which the shared information refers to the parameter controlling the sparsity. MT-FIM simultaneously minimizes the within-class scatter and maximizes the distance between different classes to enable the robust performance of each individual task. We conduct comprehensive experiments on three face image databases. The experimental results show that our algorithm outperforms the state-of-the-art algorithms.

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http://hdl.handle.net/10453/122660