Iris Recognition Using Image Moments and k-Means Algorithm

This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm. The disk shaped area of the iris is transformed into a rectangular form. Described moments are extracted fro...

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Published in:TheScientificWorld 2014-01, Vol.2014 (2014), p.1-9
Main Authors: Islam, Saeed, Ahmad, Farooq, Khan, Sher Afzal, Khan, Yaser Daanial
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Biometrics
Biometry - methods
Classification
Colleges & universities
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Iridology
Iris - anatomy & histology
Methods
Pattern Recognition, Automated - methods
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creator Islam, Saeed
Ahmad, Farooq
Khan, Sher Afzal
Khan, Yaser Daanial
description This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm. The disk shaped area of the iris is transformed into a rectangular form. Described moments are extracted from the grayscale image which yields a feature vector containing scale, rotation, and translation invariant moments. Images are clustered using the k-means algorithm and centroids for each cluster are computed. An arbitrary image is assumed to belong to the cluster whose centroid is the nearest to the feature vector in terms of Euclidean distance computed. The described model exhibits an accuracy of 98.5%.
doi_str_mv 10.1155/2014/723595
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The iris is first segmented from the acquired image of an eye using an edge detection algorithm. The disk shaped area of the iris is transformed into a rectangular form. Described moments are extracted from the grayscale image which yields a feature vector containing scale, rotation, and translation invariant moments. Images are clustered using the k-means algorithm and centroids for each cluster are computed. An arbitrary image is assumed to belong to the cluster whose centroid is the nearest to the feature vector in terms of Euclidean distance computed. 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subjects Algorithms
Artificial Intelligence
Biometrics
Biometry - methods
Classification
Colleges & universities
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Iridology
Iris - anatomy & histology
Methods
Pattern Recognition, Automated - methods
title Iris Recognition Using Image Moments and k-Means Algorithm
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