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Iris Feature Extraction and Recognition based on Gray Level Co-occurrence Matrix (GLCM) Technique

by Rabab A. Rasool
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 25
Year of Publication: 2018
Authors: Rabab A. Rasool
10.5120/ijca2018917826

Rabab A. Rasool . Iris Feature Extraction and Recognition based on Gray Level Co-occurrence Matrix (GLCM) Technique. International Journal of Computer Applications. 181, 25 ( Nov 2018), 15-17. DOI=10.5120/ijca2018917826

@article{ 10.5120/ijca2018917826,
author = { Rabab A. Rasool },
title = { Iris Feature Extraction and Recognition based on Gray Level Co-occurrence Matrix (GLCM) Technique },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2018 },
volume = { 181 },
number = { 25 },
month = { Nov },
year = { 2018 },
issn = { 0975-8887 },
pages = { 15-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number25/30091-2018917826/ },
doi = { 10.5120/ijca2018917826 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:07:03.706520+05:30
%A Rabab A. Rasool
%T Iris Feature Extraction and Recognition based on Gray Level Co-occurrence Matrix (GLCM) Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 25
%P 15-17
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Biometric features have received great attention for many applications. Iris recognition is one of the most modern biometric technique that is used for accurate and reliable authentication. Recently, Gray-Level Cooccurrence Matrix (GLCM) is one of the advanced techniques used for features extraction. In this paper, an iris recognition system proposed involves; preprocessing, feature extraction, and matching processes. After the preprocessing process, the feature extraction technique based on GLCM has been applied to pure iris region to extract features. Only one of the second-order statistical features known as contrast will be calculated from the generated co-occurrence matrix and stored it as a numerical feature vector in CASIA-v4.0-iris database. During recognition, the matching metric based on Euclidean distance has been used for authentication. Results have demonstrated (99.5%) highly accuracy rate with (0.02) FAR, and (0.01) FRR.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Gray Level Co-occurrence Matrix (GLCM) Feature extraction Euclidean distance Iris recognition system.

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