Local face sketch synthesis learning

Gao, X; Zhong, J; Tao, D; Li, X

Local face sketch synthesis learning

Gao, X Zhong, J Tao, D

Li, X

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2008, 71 (10-12), pp. 1921 - 1930
Issue Date:: 2008-06-01

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Field	Value	Language
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Zhong, J	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2008-06-01	en_US
dc.identifier.citation	Neurocomputing, 2008, 71 (10-12), pp. 1921 - 1930	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15085
dc.description.abstract	Facial sketch synthesis (FSS) is crucial in sketch-based face recognition. This paper proposes an automatic FSS algorithm with local strategy based on embedded hidden Markov model (E-HMM) and selective ensemble (SE). By using E-HMM to model the nonlinear relationship between a photo-sketch patch pair, a series of pseudo-sketch patches, generated based on several learned models for a given photo patch, are integrated with SE strategy to synthesize a finer face pseudo-sketch patch. Finally, the intact pseudo-sketch can be generated by combining all synthesized patches. Experimental results illustrate that the proposed FSS algorithm works well. © 2008 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2007.10.025	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Local face sketch synthesis learning	en_US
dc.type	Journal Article
utslib.citation.volume	10-12	en_US
utslib.citation.volume	71	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.issue	10-12	en_US
pubs.publication-status	Published	en_US
pubs.volume	71	en_US

Abstract:

Facial sketch synthesis (FSS) is crucial in sketch-based face recognition. This paper proposes an automatic FSS algorithm with local strategy based on embedded hidden Markov model (E-HMM) and selective ensemble (SE). By using E-HMM to model the nonlinear relationship between a photo-sketch patch pair, a series of pseudo-sketch patches, generated based on several learned models for a given photo patch, are integrated with SE strategy to synthesize a finer face pseudo-sketch patch. Finally, the intact pseudo-sketch can be generated by combining all synthesized patches. Experimental results illustrate that the proposed FSS algorithm works well. © 2008 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/15085