Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion
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)...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/157173 |
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| _version_ | 1832548996451663872 |
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| author | Ying Chen Yuanning Liu Xiaodong Zhu Fei He Hongye Wang Ning Deng |
| author_facet | Ying Chen Yuanning Liu Xiaodong Zhu Fei He Hongye Wang Ning Deng |
| author_sort | Ying Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-702de4ee3cdb4b8abe636fb2838070af |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-702de4ee3cdb4b8abe636fb2838070af2025-02-03T06:12:29ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/157173157173Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion FusionYing Chen0Yuanning Liu1Xiaodong Zhu2Fei He3Hongye Wang4Ning Deng5College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaIn 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.http://dx.doi.org/10.1155/2014/157173 |
| spellingShingle | Ying Chen Yuanning Liu Xiaodong Zhu Fei He Hongye Wang Ning Deng Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion The Scientific World Journal |
| title | Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion |
| title_full | Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion |
| title_fullStr | Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion |
| title_full_unstemmed | Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion |
| title_short | Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion |
| title_sort | efficient iris recognition based on optimal subfeature selection and weighted subregion fusion |
| url | http://dx.doi.org/10.1155/2014/157173 |
| work_keys_str_mv | AT yingchen efficientirisrecognitionbasedonoptimalsubfeatureselectionandweightedsubregionfusion AT yuanningliu efficientirisrecognitionbasedonoptimalsubfeatureselectionandweightedsubregionfusion AT xiaodongzhu efficientirisrecognitionbasedonoptimalsubfeatureselectionandweightedsubregionfusion AT feihe efficientirisrecognitionbasedonoptimalsubfeatureselectionandweightedsubregionfusion AT hongyewang efficientirisrecognitionbasedonoptimalsubfeatureselectionandweightedsubregionfusion AT ningdeng efficientirisrecognitionbasedonoptimalsubfeatureselectionandweightedsubregionfusion |