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Robust Iris Recognition based on Orientation Field

by Pravin S. Patil
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 19
Year of Publication: 2014
Authors: Pravin S. Patil
10.5120/16703-6844

Pravin S. Patil . Robust Iris Recognition based on Orientation Field. International Journal of Computer Applications. 95, 19 ( June 2014), 19-24. DOI=10.5120/16703-6844

@article{ 10.5120/16703-6844,
author = { Pravin S. Patil },
title = { Robust Iris Recognition based on Orientation Field },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 19 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number19/16703-6844/ },
doi = { 10.5120/16703-6844 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:19:52.541571+05:30
%A Pravin S. Patil
%T Robust Iris Recognition based on Orientation Field
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 19
%P 19-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recently, with an increasing demand on security biometric personal identification has been receiving extensive attention. Among many biometrics techniques, iris recognition is one of the most promising approached due to its high reliability. This paper present a robust iris recognition technique based on orientation field. Initially the eye images have been localized in circular form by using Hough transform. Than flat bed form of iris is generated using Daugman's homogeneous rubber sheet model. From the view point of texture analysis, the local spatial patterns in an iris mainly involve orientation and frequency information. Local image structure plays an important role in iris recognition, so an intuitive idea of iris pattern representation is based on the geometric structure of image data in small region. In our work localized iris image is divided into number of non-overlapping blocks. The blocks orientation has been determined from the pixel gradient orientation based on averaging and optimization. The gradient magnitudes have been computed using Sobel gradient operators. The variance of orientation has been used as feature vector. L2 norm is used to compute a similarity score for the query and template images. The performance of the implemented algorithms has been evaluated using Receiver Operating Characteristics (ROC) and experimental results are reported.

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Index Terms

Computer Science
Information Sciences

Keywords

Iris Image Preprocessing Orientation field Feature Variance Receiver Operating Characteristics

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