Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata

Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accurac...

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Main Authors: Ghanizadeh, A., Abarghouei, A. A., Sinaie, S., Saad, Puteh, Shamsuddin, Siti Mariyam
Format: Article
Published: Optical Society of America 2011
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Online Access:http://eprints.utm.my/id/eprint/29240/
http://dx.doi.org/10.1364/AO.50.003191
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spelling my.utm.292402019-03-25T08:06:39Z http://eprints.utm.my/id/eprint/29240/ Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata Ghanizadeh, A. Abarghouei, A. A. Sinaie, S. Saad, Puteh Shamsuddin, Siti Mariyam QA75 Electronic computers. Computer science Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods. Optical Society of America 2011-07 Article PeerReviewed Ghanizadeh, A. and Abarghouei, A. A. and Sinaie, S. and Saad, Puteh and Shamsuddin, Siti Mariyam (2011) Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata. Applied Optics, 50 (19). pp. 3191-3200. ISSN 1559-128X http://dx.doi.org/10.1364/AO.50.003191 DOI:10.1364/AO.50.003191
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ghanizadeh, A.
Abarghouei, A. A.
Sinaie, S.
Saad, Puteh
Shamsuddin, Siti Mariyam
Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata
description Iris-based biometric systems identify individuals based on the characteristics of their iris, since they are proven to remain unique for a long time. An iris recognition system includes four phases, the most important of which is preprocessing in which the iris segmentation is performed. The accuracy of an iris biometric system critically depends on the segmentation system. In this paper, an iris segmentation system using edge detection techniques and Hough transforms is presented. The newly proposed edge detection system enhances the performance of the segmentation in a way that it performs much more efficiently than the other conventional iris segmentation methods.
format Article
author Ghanizadeh, A.
Abarghouei, A. A.
Sinaie, S.
Saad, Puteh
Shamsuddin, Siti Mariyam
author_facet Ghanizadeh, A.
Abarghouei, A. A.
Sinaie, S.
Saad, Puteh
Shamsuddin, Siti Mariyam
author_sort Ghanizadeh, A.
title Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata
title_short Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata
title_full Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata
title_fullStr Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata
title_full_unstemmed Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata
title_sort iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata
publisher Optical Society of America
publishDate 2011
url http://eprints.utm.my/id/eprint/29240/
http://dx.doi.org/10.1364/AO.50.003191
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score 13.2014675