Face sketch-photo synthesis under multi-dictionary sparse representation framework

Wang, N; Gao, X; Tao, D; Li, X

Face sketch-photo synthesis under multi-dictionary sparse representation framework

Wang, N Gao, X Tao, D

Li, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 6th International Conference on Image and Graphics, ICIG 2011, 2011, pp. 82 - 87
Issue Date:: 2011-09-26

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Field	Value	Language
dc.contributor.author	Wang, N	en_US
dc.contributor.author	Gao, X	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	2011-09-26	en_US
dc.identifier.citation	Proceedings - 6th International Conference on Image and Graphics, ICIG 2011, 2011, pp. 82 - 87	en_US
dc.identifier.isbn	9780769545417	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120480
dc.description.abstract	Sketch-photo synthesis is one of the important research issues of heterogeneous image transformation. Some available popular synthesis methods, like locally linear embedding (LLE), usually generate sketches or photos with lower definition and blurred details, which reduces the visual quality and the recognition rate across the heterogeneous images. In order to improve the quality of the synthesized images, a multi-dictionary sparse representation based face sketch-photo synthesis model is constructed. In the proposed model, LLE is used to estimate an initial sketch or photo, while the multi-dictionary sparse representation model is applied to generate the high frequency and detail information. Finally, by linear superimposing, the enhanced face sketch or photo can be obtained. Experimental results show that sketches and photos synthesized by the proposed method have higher definition and much richer detail information resulting in a higher face recognition rate between sketches and photos. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - 6th International Conference on Image and Graphics, ICIG 2011	en_US
dc.relation.isbasedon	10.1109/ICIG.2011.112	en_US
dc.title	Face sketch-photo synthesis under multi-dictionary sparse representation framework	en_US
dc.type	Conference Proceeding
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

Abstract:

Sketch-photo synthesis is one of the important research issues of heterogeneous image transformation. Some available popular synthesis methods, like locally linear embedding (LLE), usually generate sketches or photos with lower definition and blurred details, which reduces the visual quality and the recognition rate across the heterogeneous images. In order to improve the quality of the synthesized images, a multi-dictionary sparse representation based face sketch-photo synthesis model is constructed. In the proposed model, LLE is used to estimate an initial sketch or photo, while the multi-dictionary sparse representation model is applied to generate the high frequency and detail information. Finally, by linear superimposing, the enhanced face sketch or photo can be obtained. Experimental results show that sketches and photos synthesized by the proposed method have higher definition and much richer detail information resulting in a higher face recognition rate between sketches and photos. © 2011 IEEE.

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http://hdl.handle.net/10453/120480