Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion

22Citations
Citations of this article
26Readers
Mendeley users who have this article in their library.

This article is free to access.

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, 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. © 2014 Ying Chen et al.

Cite

CITATION STYLE

APA

Chen, Y., Liu, Y., Zhu, X., He, F., Wang, H., & Deng, N. (2014). Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion. The Scientific World Journal, 2014. https://doi.org/10.1155/2014/157173

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free