Toggle Main Menu Toggle Search

ePrints

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

Lookup NU author(s): sinan Alkassar, Dr Wai Lok Woo, Professor Satnam Dlay, Professor Jonathon Chambers

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


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.


Publication metadata

Author(s): Alkassar S, Woo WL, Dlay SS, Chambers JA

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 9th International Conference on Biometrics (ICB)

Year of Conference: 2016

Online publication date: 25/08/2016

Acceptance date: 01/01/1900

Publisher: IEEE

URL: https://doi.org/10.1109/ICB.2016.7550049

DOI: 10.1109/ICB.2016.7550049

Library holdings: Search Newcastle University Library for this item

ISBN: 9781509018697


Actions

    Link to this publication


Share