Human Iris Verification.
Date of Submission
December 2011
Date of Award
Winter 12-12-2012
Institute Name (Publisher)
Indian Statistical Institute
Document Type
Master's Dissertation
Degree Name
Master of Technology
Subject Name
Computer Science
Department
Electronics and Communication Sciences Unit (ECSU-Kolkata)
Supervisor
Chanda, Bhabatosh (ECSU-Kolkata; ISI)
Abstract (Summary of the Work)
This article proposes a method for personal identification based on iris recognition. The method consists of three major components: image preprocessing, feature extraction and classifier design. The UBIRIS database is used for obtaining iris images. The iris segmentation is obtained by using an integro-differential operation.The segmented iris is then normalised and a small portion of the normalised portion is used for feature extraction and verification.Three types of features are extracted from the normalised iris segments - GLCM based features, features based on number of runs of pixels in four directions (N,NE,E,NW) and features extracted using Local Directional Pattern or LDP.We present a comparison of the performances of the method using a combination of the above mentioned feature extraction techniques.It has also been shown experimentally that the iris patterns exhibit a symmetry about the vertical axis. The multiclass problem is reduced to a two class verification problem. Two types of feature vectors - interclass difference vectors and intraclass difference vectors, thus created, are trained on a Support Vector Machines for classification. Experimental results show that the proposed method has encouraging performance.
Control Number
ISI-DISS-2011-287
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
DOI
http://dspace.isical.ac.in:8080/jspui/handle/10263/6443
Recommended Citation
Mukherjee, Suvadip, "Human Iris Verification." (2012). Master’s Dissertations. 161.
https://digitalcommons.isical.ac.in/masters-dissertations/161
Comments
ProQuest Collection ID: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:28843182