DSW feature based Hidden Marcov Model: An application on object identification

Zheng, L; Taiqing, W; Shengjin, W; Xiaoqing, D

DSW feature based Hidden Marcov Model: An application on object identification

Zheng, L

Taiqing, W Shengjin, W Xiaoqing, D

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011, 2011, pp. 502 - 506
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Zheng, L https://orcid.org/0000-0002-1464-9500	en_US
dc.contributor.author	Taiqing, W	en_US
dc.contributor.author	Shengjin, W	en_US
dc.contributor.author	Xiaoqing, D	en_US
dc.date	2011-10-14	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011, 2011, pp. 502 - 506	en_US
dc.identifier.isbn	978-1-4577-1196-1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118088
dc.description.abstract	This paper proposes to perform palmprint identification with Hidden Markov Models (HMM). Palmprint identification, as an emerging biometric technology, has been extensively investigated in the last decade. Due to its low-price capture device, fast implementation speed and high accuracy, palmprint identification is very competitive in biometric research area. Currently, the majority of literatures focus on palm line extraction algorithms and coding schemes, with little attention on classifier design. In this paper, Down-sliding Window (DSW) technique is employed to create a highcorrelated feature sequence while palmprint is featured by simple down-sampled images. One-to-50 experiment demonstrates that HMM with single component and six states give the best overall performance 99.80%, which indicates the feasibility of HMMs for tasks in palmprint identification.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of the 2011 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2011	en_US
dc.relation.ispartof	International Conference of Soft Computing and Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/SoCPaR.2011.6089146	en_US
dc.rights	© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	DSW feature based Hidden Marcov Model: An application on object identification	en_US
dc.type	Conference Proceeding
utslib.location.activity	Dalian, China	en_US
utslib.for	080106 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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.finish-date	2011-10-16	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2011-10-14	en_US

Abstract:

This paper proposes to perform palmprint identification with Hidden Markov Models (HMM). Palmprint identification, as an emerging biometric technology, has been extensively investigated in the last decade. Due to its low-price capture device, fast implementation speed and high accuracy, palmprint identification is very competitive in biometric research area. Currently, the majority of literatures focus on palm line extraction algorithms and coding schemes, with little attention on classifier design. In this paper, Down-sliding Window (DSW) technique is employed to create a highcorrelated feature sequence while palmprint is featured by simple down-sampled images. One-to-50 experiment demonstrates that HMM with single component and six states give the best overall performance 99.80%, which indicates the feasibility of HMMs for tasks in palmprint identification.

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http://hdl.handle.net/10453/118088