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A Colour Iris Recognition System Employing Multiple Classifier Techniques

Radu, Petru, Sirlantzis, Konstantinos, Howells, Gareth, Hoque, Sanaul, Deravi, Farzin (2013) A Colour Iris Recognition System Employing Multiple Classifier Techniques. ELCVIA Electronic Letters on Computer Vision and Image Analysis, 12 (2). pp. 54-65. ISSN 1577-5097. (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://elcvia.cvc.uab.es/article/view/520

Abstract

The randomness of iris texture has allowed researchers to develop biometric systems with almost flawless accuracies. However, a common drawback of the majority of existing iris recognition systems is the constrained environment in which the user is enroled and recognized. The iris recognition systems typically require a high quality iris image captured under near infrared illumination. A desirable property of an iris recognition system is to be able to operate on colour images, whilst maintaining a high accuracy. In the present work we propose an iris recognition methodology which is designed to cope with noisy colour iris images. There are two main contributions of this paper: first, we adapt standard iris features proposed in literature for near infrared images by applying a feature selection method on features extracted from various colour channels; second, we introduce a Multiple Classifier System architecture to enhance the recognition accuracy of the biometric system. With a feature size of only 360 real valued components, the proposed iris recognition system performs with a high accuracy on UBIRISv1 dataset, in both identification and verfication scenarios.

Item Type: Article
Uncontrolled keywords: Multiple Classifier System; Colour Iris Recognition; Principal Component Analysis; Biometrics;
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: 18 Oct 2013 13:47 UTC
Last Modified: 01 Aug 2019 10:36 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35526 (The current URI for this page, for reference purposes)
Hoque, Sanaul: https://orcid.org/0000-0001-8627-3429
Deravi, Farzin: https://orcid.org/0000-0003-0885-437X
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