title: Novel approaches to improve iris recognition system performance based on local quality evaluation and feature fusion.
abstract: For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.
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.
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 Physics and Electronic Information, Wenzhou University, Zhejiang 325035, China.
authors.forename: Huiling
authors.identifier: 0000-0003-1334-9722
authors.initials: H
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: 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: Yutong
authors.identifier: 0000-0003-4829-4741
authors.initials: Y
authors.lastname: Pang
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D007498:Iris / Q000502:physiology*; D008954:Models, Biological