Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion
Joint Authors
Chen, Ying
Liu, Yuanning
Zhu, Xiaodong
He, Fei
Wang, Hongye
Deng, Ning
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-10
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system.
Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail.
Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature.
Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion.
Particle swarm optimization is utilized to accelerate achieve different sub-region’s weights and then weighted different subregions’ matching scores to generate the final decision.
The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.
American Psychological Association (APA)
Chen, Ying& Liu, Yuanning& Zhu, Xiaodong& He, Fei& Wang, Hongye& Deng, Ning. 2014. Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-19.
https://search.emarefa.net/detail/BIM-1048511
Modern Language Association (MLA)
Chen, Ying…[et al.]. Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion. The Scientific World Journal No. 2014 (2014), pp.1-19.
https://search.emarefa.net/detail/BIM-1048511
American Medical Association (AMA)
Chen, Ying& Liu, Yuanning& Zhu, Xiaodong& He, Fei& Wang, Hongye& Deng, Ning. Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-19.
https://search.emarefa.net/detail/BIM-1048511
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1048511