Sclera recognition using dense-SIFT

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

Sclera recognition using dense-SIFT

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

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Publication Type:: Conference Proceeding
Citation:: International Conference on Intelligent Systems Design and Applications, ISDA, 2014, pp. 74 - 79
Issue Date:: 2014-10-10

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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	2014-10-10	en_US
dc.identifier.citation	International Conference on Intelligent Systems Design and Applications, ISDA, 2014, pp. 74 - 79	en_US
dc.identifier.isbn	9781479935161	en_US
dc.identifier.issn	2164-7143	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121135
dc.description.abstract	© 2013 IEEE. In this paper we propose a biometric sclera recognition and validation system. Here the sclera segmentation is performed bya time-adaptive active contour-based region growing technique. The sclera vessels are not prominent so image enhancement is required and hence a bank of 2D decomposition. A Haar wavelet multi-resolution filter is used to enhance the vessels pattern for better accuracy. For feature extraction, Dense Scale Invariant Feature Transform (D-SIFT) is used. D-SIFT patch descriptors of each training image are used to form bag of features by using k-means clustering and a spatial pyramid model, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. Anencouraging Equal Error Rate (EER) of 0.66% is attained in the experiments presented.	en_US
dc.relation.ispartof	International Conference on Intelligent Systems Design and Applications, ISDA	en_US
dc.relation.isbasedon	10.1109/ISDA.2013.6920711	en_US
dc.title	Sclera recognition using dense-SIFT	en_US
dc.type	Conference Proceeding
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.publication-status	Published	en_US

Abstract:

© 2013 IEEE. In this paper we propose a biometric sclera recognition and validation system. Here the sclera segmentation is performed bya time-adaptive active contour-based region growing technique. The sclera vessels are not prominent so image enhancement is required and hence a bank of 2D decomposition. A Haar wavelet multi-resolution filter is used to enhance the vessels pattern for better accuracy. For feature extraction, Dense Scale Invariant Feature Transform (D-SIFT) is used. D-SIFT patch descriptors of each training image are used to form bag of features by using k-means clustering and a spatial pyramid model, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. Anencouraging Equal Error Rate (EER) of 0.66% is attained in the experiments presented.

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http://hdl.handle.net/10453/121135