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

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)

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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.

Item Type: Article
Uncontrolled Keywords: Discrete cosine transforms; image fusion; image recognition; image matching; feature extraction; discrete wavelet transforms; iris recognition; biometrics (access control); visual databases
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 20 Feb 2019 07:18
Last Modified: 20 Feb 2019 07:18
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