Enhanced segmentation and complex-sclera features for human recognition with unconstrained visible-wavelength imaging

Alkassar, S., Woo, Wai Lok, Dlay, Satnam and Chambers, Jonathon (2016) Enhanced segmentation and complex-sclera features for human recognition with unconstrained visible-wavelength imaging. In: 2016 International Conference on Biometrics (ICB). IEEE. ISBN 978-1-5090-1870-3

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Abstract

Sclera recognition has received attention recently due to the distinctive features extracted from blood vessels within the sclera. However, uncontrolled human pose, multiple iris gaze directions, different eye image capturing distance and variation in lighting conditions lead to many challenges in sclera recognition. Therefore, we propose an enhanced system for sclera recognition with visible-wavelength eye images captured in unconstrained conditions. The proposed segmentation algorithm fuses multiple color space skin classifiers to overcome the noise factors introduced through acquiring sclera images such as motion, blur, gaze and rotation. We also propose a blood vessel enhancement and feature extraction method which we denote as complex-sclera features to increase the adaptability to noisy blood vessel deformations. The proposed system is evaluated using UBIRIS.v1, UBIRIS.v2 and UTIRIS databases and the results are promising in terms of accuracy and suitability in real-time applications due to low processing times.

Item Type:	Book Section
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	04 Apr 2019 14:33
Last Modified:	10 Oct 2019 20:34
URI:	http://nrl.northumbria.ac.uk/id/eprint/38762

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