A new efficient and adaptive sclera recognition system

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

A new efficient and adaptive sclera recognition 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. 1 - 8
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. 1 - 8	en_US
dc.identifier.isbn	9781479945344	en_US
dc.identifier.issn	2325-4300	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121092
dc.description.abstract	© 2014 IEEE. In this paper an efficient and adaptive biometric sclera recognition and verification system is proposed. Sclera segmentation was performed by Fuzzy C-means clustering. Since the sclera vessels are not prominent, in order to make them clearly visible image enhancement was required. Adaptive histogram equalization, followed by a bank of Discrete Meyer Wavelet was used to enhance the sclera vessel patterns. Feature extraction was performed by, Dense Local Directional Pattern (D-LDP). D-LDP patch descriptors of each training image are used to form a bag of features; further Spatial Pyramid Matching was used to produce the final training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset was used here for experimentation of the proposed system. To investigate regarding sclera patterns adaptively with respect to change in environmental condition, population, data accruing technique and time span two different session of the mention dataset are utilized. The images in two sessions are different in acquiring technique, representation, number of individual and they were captured in a gap of two weeks. An encouraging Equal Error Rate (EER) of 3.95% was achieved in the above mention investigation.	en_US
dc.relation.ispartof	IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, CIBIM	en_US
dc.relation.isbasedon	10.1109/CIBIM.2014.7015436	en_US
dc.title	A new efficient and adaptive sclera recognition 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 paper an efficient and adaptive biometric sclera recognition and verification system is proposed. Sclera segmentation was performed by Fuzzy C-means clustering. Since the sclera vessels are not prominent, in order to make them clearly visible image enhancement was required. Adaptive histogram equalization, followed by a bank of Discrete Meyer Wavelet was used to enhance the sclera vessel patterns. Feature extraction was performed by, Dense Local Directional Pattern (D-LDP). D-LDP patch descriptors of each training image are used to form a bag of features; further Spatial Pyramid Matching was used to produce the final training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset was used here for experimentation of the proposed system. To investigate regarding sclera patterns adaptively with respect to change in environmental condition, population, data accruing technique and time span two different session of the mention dataset are utilized. The images in two sessions are different in acquiring technique, representation, number of individual and they were captured in a gap of two weeks. An encouraging Equal Error Rate (EER) of 3.95% was achieved in the above mention investigation.

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http://hdl.handle.net/10453/121092