title: Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion.
abstract: 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, and MMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China ; College of Software, Nanchang Hangkong University, Nanchang 330063, China ; Internet of Things Technology Institute, Nanchang Hangkong University, Nanchang 330063, China.
authors.forename: Ying
authors.identifier: 0000-0002-1914-8780
authors.initials: Y
authors.lastname: Chen
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
authors.forename: Yuanning
authors.identifier: 0000-0003-2643-0976
authors.initials: Y
authors.lastname: Liu
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
authors.forename: Xiaodong
authors.identifier: 0000-0002-1665-7029
authors.initials: X
authors.lastname: Zhu
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
authors.forename: Fei
authors.identifier: 0000-0002-8010-7638
authors.initials: F
authors.lastname: He
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
authors.forename: Hongye
authors.identifier: 0000-0002-1951-5376
authors.initials: H
authors.lastname: Wang
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
authors.forename: Ning
authors.initials: N
authors.lastname: Deng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007498:Iris / Q000033:anatomy & histology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D013382:Subtraction Technique