A Colour Iris Recognition System Employing Multiple Classifier Techniques
Radu, Petru (University of Kent. School of Engineering and Digital Arts)
Sirlantzis, Konstantinos (University of Kent. School of Engineering and Digital Arts)
Howells, Gareth (University of Kent. School of Engineering and Digital Arts)
Hoque, Sanaul (University of Kent. School of Engineering and Digital Arts)
Deravi, Farzin (University of Kent. School of Engineering and Digital Arts)

Data:	2013
Resum:	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.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Colour Iris Recognition ; Multiple Classifier Systems ; Principal Component Analysis
Publicat a:	ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 12, Núm. 2 (2013) , p. 54-65, ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.es/article/view/v12-n2-radu
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/280908
DOI: 10.5565/rev/elcvia.520

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