Robust partitioning and indexing for iris biometric database based on local features

Explosive growth in the volume of stored biometric data has resulted in classification and indexing becoming important operations in image database systems. Consequently, researchers are focused on finding suitable features of images that can be used as indexes. Stored templates have to be classifie...

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Main Authors:	Khalaf, Emad Taha, Mohammed, Muamer N., Kohbalan, Moorthy
Format:	Article
Language:	English
Published:	The Institution of Engineering and Technology 2018
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/21527/ http://umpir.ump.edu.my/id/eprint/21527/ http://umpir.ump.edu.my/id/eprint/21527/ http://umpir.ump.edu.my/id/eprint/21527/1/Robust%20partitioning%20and%20indexing%20for%20irisbiometric%20database%20based%20on%20local%20features.pdf

id	ump-21527
recordtype	eprints
spelling	ump-215272019-02-20T07:18:47Z http://umpir.ump.edu.my/id/eprint/21527/ Robust partitioning and indexing for iris biometric database based on local features Khalaf, Emad Taha Mohammed, Muamer N. Kohbalan, Moorthy 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. Consequently, researchers are focused on finding suitable features of images that can be used as indexes. Stored templates have to be classified and indexed based on these extracted features in a manner that enables access to and retrieval of those data by efficient search processes. This paper proposes a method that extracts the most relevant features of iris images to facilitate minimisation of the indexing time and the search area of the biometric database. The proposed method combines three transformation methods DCT, DWT and SVD to analyse iris images and extract their local features. Further, the scalable K-means++ algorithm is used for partitioning and classification processes, and an efficient parallel technique that divides the features groups causing the formation of two b-trees based on index keys is applied for search and retrieval. Moreover, search within a group is achieved using a proposed half search algorithm. Experimental results on three different publicly iris databases indicate that the proposed method results in a significant performance improvement in terms of bin miss rate and penetration rate compared with conventional methods. The Institution of Engineering and Technology 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/21527/1/Robust%20partitioning%20and%20indexing%20for%20irisbiometric%20database%20based%20on%20local%20features.pdf Khalaf, Emad Taha and Mohammed, Muamer N. and Kohbalan, Moorthy (2018) Robust partitioning and indexing for iris biometric database based on local features. IET Biometrics, 7 (6). pp. 589-597. ISSN 2047-4946 https://doi.org/10.1049/iet-bmt.2017.0130 https://doi.org/10.1049/iet-bmt.2017.0130
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 Robust partitioning and indexing for iris biometric database based on local features
description	Explosive growth in the volume of stored biometric data has resulted in classification and indexing becoming important operations in image database systems. Consequently, researchers are focused on finding suitable features of images that can be used as indexes. Stored templates have to be classified and indexed based on these extracted features in a manner that enables access to and retrieval of those data by efficient search processes. This paper proposes a method that extracts the most relevant features of iris images to facilitate minimisation of the indexing time and the search area of the biometric database. The proposed method combines three transformation methods DCT, DWT and SVD to analyse iris images and extract their local features. Further, the scalable K-means++ algorithm is used for partitioning and classification processes, and an efficient parallel technique that divides the features groups causing the formation of two b-trees based on index keys is applied for search and retrieval. Moreover, search within a group is achieved using a proposed half search algorithm. Experimental results on three different publicly iris databases indicate that the proposed method results in a significant performance improvement in terms of bin miss rate and penetration rate compared with conventional methods.
format	Article
author	Khalaf, Emad Taha Mohammed, Muamer N. Kohbalan, Moorthy
author_facet	Khalaf, Emad Taha Mohammed, Muamer N. Kohbalan, Moorthy
author_sort	Khalaf, Emad Taha
title	Robust partitioning and indexing for iris biometric database based on local features
title_short	Robust partitioning and indexing for iris biometric database based on local features
title_full	Robust partitioning and indexing for iris biometric database based on local features
title_fullStr	Robust partitioning and indexing for iris biometric database based on local features
title_full_unstemmed	Robust partitioning and indexing for iris biometric database based on local features
title_sort	robust partitioning and indexing for iris biometric database based on local features
publisher	The Institution of Engineering and Technology
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/21527/ http://umpir.ump.edu.my/id/eprint/21527/ http://umpir.ump.edu.my/id/eprint/21527/ http://umpir.ump.edu.my/id/eprint/21527/1/Robust%20partitioning%20and%20indexing%20for%20irisbiometric%20database%20based%20on%20local%20features.pdf
first_indexed	2023-09-18T22:31:37Z
last_indexed	2023-09-18T22:31:37Z
_version_	1777416323708485632

Robust partitioning and indexing for iris biometric database based on local features

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