Face recognition based on modified LBP

Zhang, Z; He, X

Face recognition based on modified LBP

Zhang, Z He, X

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Publication Type:: Conference Proceeding
Citation:: ICCSE 2012 - Proceedings of 2012 7th International Conference on Computer Science and Education, 2012, pp. 160 - 164
Issue Date:: 2012-11-05

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Field	Value	Language
dc.contributor.author	Zhang, Z	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2012-11-05	en_US
dc.identifier.citation	ICCSE 2012 - Proceedings of 2012 7th International Conference on Computer Science and Education, 2012, pp. 160 - 164	en_US
dc.identifier.isbn	9781467302425	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31481
dc.description.abstract	Face recognition in unconstrained, natural conditions still remains a challenging task. As a powerful local descriptor, Local Binary Patterns has shown the advantage of representation and performance. However, it is still affected by robustness and accuracy. In this paper, a novel method is presented to improve the performance of automatic face recognition under uncontrolled conditions. We modify the conventional Local Binary Pattern and use it as a new feature descriptor. Partial Hausdorff Distance is applied as a dissimilarity measurement. Experimental results show that the proposed algorithm outperforms the traditional LBP approach in terms of accuracy rate and robustness. It can reduce the sensitivity caused by illumination variation, pose variation, occlusion etc. © 2012 IEEE.	en_US
dc.relation.ispartof	ICCSE 2012 - Proceedings of 2012 7th International Conference on Computer Science and Education	en_US
dc.relation.isbasedon	10.1109/ICCSE.2012.6295049	en_US
dc.title	Face recognition based on modified LBP	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne, Australia
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Face recognition in unconstrained, natural conditions still remains a challenging task. As a powerful local descriptor, Local Binary Patterns has shown the advantage of representation and performance. However, it is still affected by robustness and accuracy. In this paper, a novel method is presented to improve the performance of automatic face recognition under uncontrolled conditions. We modify the conventional Local Binary Pattern and use it as a new feature descriptor. Partial Hausdorff Distance is applied as a dissimilarity measurement. Experimental results show that the proposed algorithm outperforms the traditional LBP approach in terms of accuracy rate and robustness. It can reduce the sensitivity caused by illumination variation, pose variation, occlusion etc. © 2012 IEEE.

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http://hdl.handle.net/10453/31481