Efficient classifying and indexing for large iris database based on enhanced clustering method

Efficient classifying and indexing for large iris database based on enhanced clustering method

Explosive growth in the volume of stored biometric data has resulted in classification and indexing becoming important operations in image database systems. A new method is presented in this paper to extract the most relevant features of iris biometric images for indexing the iris database. Three tr...

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Main Authors: Khalaf, Emad Taha, Mohammed, Muamer N., Kohbalan, Moorthy, Khalaf, Ahmad Taha
Format: Article
Language:English
Published: ICI Bucharest 2018
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/21528/
http://umpir.ump.edu.my/id/eprint/21528/
http://umpir.ump.edu.my/id/eprint/21528/
http://umpir.ump.edu.my/id/eprint/21528/1/Efficient%20classifying%20and%20indexing%20for%20large%20iris%20database.pdf
id ump-21528
recordtype eprints
spelling ump-215282019-01-28T09:01:06Z http://umpir.ump.edu.my/id/eprint/21528/ Efficient classifying and indexing for large iris database based on enhanced clustering method Khalaf, Emad Taha Mohammed, Muamer N. Kohbalan, Moorthy Khalaf, Ahmad Taha QA76 Computer software Explosive growth in the volume of stored biometric data has resulted in classification and indexing becoming important operations in image database systems. A new method is presented in this paper to extract the most relevant features of iris biometric images for indexing the iris database. Three transformation methods DCT, DWT and SVD were used to analyse the iris image and to extract its local features. The clustering method shouldering on the responsibility of determining the partitioning and classification efficiencies of the system has been improved. In the current work, the new Weighted K-means algorithm based on the Improved Firefly Algorithm (WKIFA) has been used to overcome the shortcomings in using the Fireflies Algorithm (FA). The proposed method can be used to perform global search and exhibits quick convergence rate while optimizing the initial clustering centers of the K-means algorithm. From the experimental results, the proposed method was indeed more effective for clustering and classification and outperformed the traditional k-mean algorithm. The Penetration Rates underwent reductions and reached the levels of 0.98, 0.13 and 0.12 for three different databases. Also, the Bin Miss Rates decreased to 0.3037, 0.4226 and 0.2019 for the investigated databases. ICI Bucharest 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/21528/1/Efficient%20classifying%20and%20indexing%20for%20large%20iris%20database.pdf Khalaf, Emad Taha and Mohammed, Muamer N. and Kohbalan, Moorthy and Khalaf, Ahmad Taha (2018) Efficient classifying and indexing for large iris database based on enhanced clustering method. Studies in Informatics and Control, 27 (2). pp. 191-202. ISSN 1841-429X https://sic.ici.ro/efficient-classifying-and-indexing-for-large-iris-database-based-on-enhanced-clustering-method/ doi: https://doi.org/10.24846/v27i2y201807
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Khalaf, Emad Taha
Mohammed, Muamer N.
Kohbalan, Moorthy
Khalaf, Ahmad Taha
Efficient classifying and indexing for large iris database based on enhanced clustering method
description Explosive growth in the volume of stored biometric data has resulted in classification and indexing becoming important operations in image database systems. A new method is presented in this paper to extract the most relevant features of iris biometric images for indexing the iris database. Three transformation methods DCT, DWT and SVD were used to analyse the iris image and to extract its local features. The clustering method shouldering on the responsibility of determining the partitioning and classification efficiencies of the system has been improved. In the current work, the new Weighted K-means algorithm based on the Improved Firefly Algorithm (WKIFA) has been used to overcome the shortcomings in using the Fireflies Algorithm (FA). The proposed method can be used to perform global search and exhibits quick convergence rate while optimizing the initial clustering centers of the K-means algorithm. From the experimental results, the proposed method was indeed more effective for clustering and classification and outperformed the traditional k-mean algorithm. The Penetration Rates underwent reductions and reached the levels of 0.98, 0.13 and 0.12 for three different databases. Also, the Bin Miss Rates decreased to 0.3037, 0.4226 and 0.2019 for the investigated databases.
format Article
author Khalaf, Emad Taha
Mohammed, Muamer N.
Kohbalan, Moorthy
Khalaf, Ahmad Taha
author_facet Khalaf, Emad Taha
Mohammed, Muamer N.
Kohbalan, Moorthy
Khalaf, Ahmad Taha
author_sort Khalaf, Emad Taha
title Efficient classifying and indexing for large iris database based on enhanced clustering method
title_short Efficient classifying and indexing for large iris database based on enhanced clustering method
title_full Efficient classifying and indexing for large iris database based on enhanced clustering method
title_fullStr Efficient classifying and indexing for large iris database based on enhanced clustering method
title_full_unstemmed Efficient classifying and indexing for large iris database based on enhanced clustering method
title_sort efficient classifying and indexing for large iris database based on enhanced clustering method
publisher ICI Bucharest
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/21528/
http://umpir.ump.edu.my/id/eprint/21528/
http://umpir.ump.edu.my/id/eprint/21528/
http://umpir.ump.edu.my/id/eprint/21528/1/Efficient%20classifying%20and%20indexing%20for%20large%20iris%20database.pdf
first_indexed 2023-09-18T22:31:37Z
last_indexed 2023-09-18T22:31:37Z
_version_ 1777416323848994816

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