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A New Approach of Iris Detection and Recognition

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 7 No. 5 (2017)

DOI
10.11591/ijece.v7i5.pp2530-2536
ISSN
2088-8708, 2088-8708
Published
2017-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

This paper proposes an IRIS recognition and detection model for measuring the e-security. This proposed model consists of the following blocks: segmentation and normalization, feature encoding and feature extraction, and classification. In first phase, histogram equalization and canny edge detection is used for object detection. And then, Hough Transformation is utilized for detecting the center of the pupil of an IRIS. In second phase, Daugmen’s Rubber Sheet model and Log Gabor filter is used for normalization and encoding and as a feature extraction method GNS (Global Neighborhood Structure) map is used, finally extracted feature of GNS is feed to the SVM (Support Vector Machine) for training and testing. For our tested dataset, experimental results demonstrate 92% accuracy in real portion and 86% accuracy in imaginary portion for both eyes. In addition, our proposed model outperforms than other two conventional methods exhibiting higher accuracy.

Collaboration network of the authors

22 authors · 45 co-authorship links · 5 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • BRAC University
  • University of Dhaka
  • UNSW Canberra
  • Chittagong University of Engineering & Technology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nibras Ar RakibBRAC University1
SM Zamshed FarhanAffiliation unknown1
Md. Mashrur Bari SobhanBRAC University1
Arafat HabibBRAC University1
Rafid AhmedBRAC University1
Md. Sazzadul IslamBRAC University1
Fabian Parsia GeorgeBRAC University1
Istiaque Mannafee ShaikatBRAC University1
Prommy Sultana HossainBRAC University1
Mohammad Zavid ParvezBRAC University1
Amitabha ChakrabartyBRAC University1
Zannatul FerdoushBRAC University1
Booshra Nazifa MahmudBRAC University1
Md. Monirul IslamUniversity of Dhaka1
Mohammad Abul KashemUniversity of Dhaka1
Md. Hasan FurhadAffiliation unknown1
Ripon Kumar ChakraborttyUNSW Canberra1
Michael J. RyanAffiliation unknown1
Iqbal Hasan SarkerChittagong University of Engineering & Technology1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>A New Approach of Iris Detection and Recognition</dc:title><dc:creator>Biswas, Rubel</dc:creator><dc:creator>Uddin, Jia</dc:creator><dc:creator>Hasan, Md. Junayed</dc:creator><dc:description>&lt;jats:p&gt;This paper proposes an IRIS recognition and detection model for measuring the e-security. This proposed model consists of the following blocks: segmentation and normalization, feature encoding and feature extraction, and classification. In first phase, histogram equalization and canny edge detection is used for object detection. And then, Hough Transformation is utilized for detecting the center of the pupil of an IRIS. In second phase, Daugmen’s Rubber Sheet model and Log Gabor filter is used for normalization and encoding and as a feature extraction method GNS (Global Neighborhood Structure) map is used, finally extracted feature of GNS is feed to the SVM (Support Vector Machine) for training and testing. For our tested dataset, experimental results demonstrate 92% accuracy in real portion and 86% accuracy in imaginary portion for both eyes. In addition, our proposed model outperforms than other two conventional methods exhibiting higher accuracy.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2017-10-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v7i5.pp2530-2536</dc:identifier><dc:identifier>10.11591/ijece.v7i5.pp2530-2536</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 7 No 5 (2017); 2530</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/8205/6936</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>