A Visible Light Iris Recognition System using Colour Information

Radu, Petru and Sirlantzis, Konstantinos and Howells, Gareth and Hoque, Sanaul and Deravi, Farzin (2012) A Visible Light Iris Recognition System using Colour Information. In: 9th IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA 2012), 18-20 June 2012, Crete, Greece. (doi:https://doi.org/10.2316/P.2012.778-019) (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)

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Official URL
http://dx.doi.org/10.2316/P.2012.778-019

Abstract

The iris has been shown to be a highly reliable biometric modality with almost perfect authentication accuracy. However, a classical iris recognition system operates under near infrared illumination, which is a major constraint for a range of applications. In this paper, we propose an iris recognition system which is able to cope with noisy colour iris images by employing image processing techniques together with a Multiple Classifier System to fuse the information from various colour channels. There are two main contributions in the present work: first, we adapt standard iris features, proposed in the literature for near infrared images, to match the characteristics of colour iris images; second, we introduce a robust fusion mechanism to combine the features from various colour channels. With a feature size of only 360 real numbers, the efficiency of the proposed biometric system is demonstrated on the UBIRISv1 dataset for both identification and verification scenarios.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: Colour Iris Recognition, Multiple Classifier Systems, Principal Component Analysis
Subjects: T Technology > TJ Mechanical engineering and machinery > Intelligent control systems
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Konstantinos Sirlantzis
Date Deposited: 14 Dec 2015 01:42 UTC
Last Modified: 16 Dec 2015 17:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/53310 (The current URI for this page, for reference purposes)
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