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. (doi:10.5565/rev/elcvia.520) (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) (KAR id:35526)
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. | |
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 |
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DOI/Identification number: | 10.5565/rev/elcvia.520 |
Uncontrolled keywords: | Multiple Classifier System; Colour Iris Recognition; Principal Component Analysis; Biometrics; |
Subjects: | T Technology |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | Konstantinos Sirlantzis |
Date Deposited: | 18 Oct 2013 13:47 UTC |
Last Modified: | 05 Nov 2024 10:19 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/35526 (The current URI for this page, for reference purposes) |
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