Skip to main content

Improving colour iris segmentation using a model selection technique

Hu, Yang, Sirlantzis, Konstantinos, Howells, Gareth (2015) Improving colour iris segmentation using a model selection technique. Pattern Recognition Letters, 57 . pp. 24-32. ISSN 0167-8655. (doi:10.1016/j.patrec.2014.12.012) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://doi.org/10.1016/j.patrec.2014.12.012

Abstract

In this paper, we propose a novel method to improve the reliability and accuracy of colour iris segmentation for captures both from static and mobile devices. Our method is a fusion strategy based on selection among the segmentation outcomes of different segmentation methods or models. First, we present and analyse an iris segmentation framework which uses three different models to show that improvements can be obtained by selection among the outcomes generated by the three models. Then, we introduce a model selection method which defines the optimal segmentation based on a ring-shaped region around the outer segmentation boundary identified by each model. We use the histogram of oriented gradients (HOG) as features extracted from the ring-shaped region, and train a SVM-based classifier which provides the selection decision. Experiments on colour iris datasets, captured by mobile devices and static camera, show that the proposed method achieves an improved performance compared to the individual iris segmentation models and existing algorithms.

Item Type: Article
DOI/Identification number: 10.1016/j.patrec.2014.12.012
Uncontrolled keywords: Colour iris segmentation; HOG; Model selection
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Konstantinos Sirlantzis
Date Deposited: 01 Sep 2015 08:45 UTC
Last Modified: 29 May 2019 15:58 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50339 (The current URI for this page, for reference purposes)
  • Depositors only (login required):