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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| Format: | Article |
| Language: | English |
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The Institution of Engineering and Technology
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/21527/1/Robust%20partitioning%20and%20indexing%20for%20irisbiometric%20database%20based%20on%20local%20features.pdf |
| _version_ | 1825812141164199936 |
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| author | Khalaf, Emad Taha Mohammed, Muamer N. Kohbalan, Moorthy |
| author_facet | Khalaf, Emad Taha Mohammed, Muamer N. Kohbalan, Moorthy |
| author_sort | Khalaf, Emad Taha |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T12:24:44Z |
| format | Article |
| id | UMPir21527 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:24:44Z |
| publishDate | 2018 |
| publisher | The Institution of Engineering and Technology |
| record_format | dspace |
| spelling | UMPir215272019-02-20T07:18:47Z 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. (Published) https://doi.org/10.1049/iet-bmt.2017.0130 https://doi.org/10.1049/iet-bmt.2017.0130 |
| spellingShingle | QA76 Computer software Khalaf, Emad Taha Mohammed, Muamer N. Kohbalan, Moorthy Robust partitioning and indexing for iris biometric database based on local features |
| title | 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_short | 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 |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/21527/1/Robust%20partitioning%20and%20indexing%20for%20irisbiometric%20database%20based%20on%20local%20features.pdf |
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