A new wrist vein biometric system

Das, A; Pal, U; Ballester, MAF; Blumenstein, M

A new wrist vein biometric system

Das, A Pal, U Ballester, MAF Blumenstein, M

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Publication Type:: Conference Proceeding
Citation:: IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, CIBIM, 2015, 2015-January (January), pp. 68 - 75
Issue Date:: 2015-01-19

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Field	Value	Language
dc.contributor.author	Das, A	en_US
dc.contributor.author	Pal, U	en_US
dc.contributor.author	Ballester, MAF	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.issued	2015-01-19	en_US
dc.identifier.citation	IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, CIBIM, 2015, 2015-January (January), pp. 68 - 75	en_US
dc.identifier.isbn	9781479945344	en_US
dc.identifier.issn	2325-4300	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121097
dc.description.abstract	© 2014 IEEE. In this piece of work a wrist vein pattern recognition and verification system is proposed. Here the wrist vein images from the PUT database were used, which were acquired in visible spectrum. The vein image only highlights the vein pattern area so, segmentation was not required. Since the wrist's veins are not prominent, image enhancement was performed. An Adaptive Histogram Equalization and Discrete Meyer Wavelet were used to enhance the vessel patterns. For feature extraction, the vein pattern is characterized with Dense Local Binary Pattern (D-LBP). D-LBP patch descriptors of each training image are used to form a bag of features, which was used to produce the training model. Support Vector Machines (SVMs) were used for classification. An encouraging Equal Error Rate (EER) of 0.79% was achieved in our experiments.	en_US
dc.relation.ispartof	IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, CIBIM	en_US
dc.relation.isbasedon	10.1109/CIBIM.2014.7015445	en_US
dc.title	A new wrist vein biometric system	en_US
dc.type	Conference Proceeding
utslib.citation.volume	January	en_US
utslib.citation.volume	2015-January	en_US
utslib.for	0803 Computer Software	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/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.issue	January	en_US
pubs.publication-status	Published	en_US
pubs.volume	2015-January	en_US

Abstract:

© 2014 IEEE. In this piece of work a wrist vein pattern recognition and verification system is proposed. Here the wrist vein images from the PUT database were used, which were acquired in visible spectrum. The vein image only highlights the vein pattern area so, segmentation was not required. Since the wrist's veins are not prominent, image enhancement was performed. An Adaptive Histogram Equalization and Discrete Meyer Wavelet were used to enhance the vessel patterns. For feature extraction, the vein pattern is characterized with Dense Local Binary Pattern (D-LBP). D-LBP patch descriptors of each training image are used to form a bag of features, which was used to produce the training model. Support Vector Machines (SVMs) were used for classification. An encouraging Equal Error Rate (EER) of 0.79% was achieved in our experiments.

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http://hdl.handle.net/10453/121097