Cover Image

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...

Full description

Saved in:
Bibliographic Details
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/21527/1/Robust%20partitioning%20and%20indexing%20for%20irisbiometric%20database%20based%20on%20local%20features.pdf
http://umpir.ump.edu.my/id/eprint/21527/
https://doi.org/10.1049/iet-bmt.2017.0130
https://doi.org/10.1049/iet-bmt.2017.0130
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.21527
record_format eprints
spelling my.ump.umpir.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
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/21527/1/Robust%20partitioning%20and%20indexing%20for%20irisbiometric%20database%20based%20on%20local%20features.pdf
http://umpir.ump.edu.my/id/eprint/21527/
https://doi.org/10.1049/iet-bmt.2017.0130
https://doi.org/10.1049/iet-bmt.2017.0130
_version_ 1643669151054561280
score 13.211869

Similar Items