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Eye centre localisation: an unsupervised modular approach

Wenhao Zhang (Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, Bristol, UK)
Melvyn Lionel Smith (Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, Bristol, UK)
Lyndon Neal Smith (Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, Bristol, UK)
Abdul Rehman Farooq (Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, Bristol, UK)

Sensor Review

ISSN: 0260-2288

Article publication date: 20 June 2016

Abstract

Purpose

This paper aims to introduce an unsupervised modular approach for eye centre localisation in images and videos following a coarse-to-fine, global-to-regional scheme. The design of the algorithm aims at excellent accuracy, robustness and real-time performance for use in real-world applications.

Design/methodology/approach

A modular approach has been designed that makes use of isophote and gradient features to estimate eye centre locations. This approach embraces two main modalities that progressively reduce global facial features to local levels for more precise inspections. A novel selective oriented gradient (SOG) filter has been specifically designed to remove strong gradients from eyebrows, eye corners and self-shadows, which sabotage most eye centre localisation methods. The proposed algorithm, tested on the BioID database, has shown superior accuracy.

Findings

The eye centre localisation algorithm has been compared with 11 other methods on the BioID database and six other methods on the GI4E database. The proposed algorithm has outperformed all the other algorithms in comparison in terms of localisation accuracy while exhibiting excellent real-time performance. This method is also inherently robust against head poses, partial eye occlusions and shadows.

Originality/value

The eye centre localisation method uses two mutually complementary modalities as a novel, fast, accurate and robust approach. In addition, other than assisting eye centre localisation, the SOG filter is able to resolve general tasks regarding the detection of curved shapes. From an applied point of view, the proposed method has great potentials in benefiting a wide range of real-world human-computer interaction (HCI) applications.

Keywords

Citation

Zhang, W., Smith, M.L., Smith, L.N. and Farooq, A.R. (2016), "Eye centre localisation: an unsupervised modular approach", Sensor Review, Vol. 36 No. 3, pp. 277-286. https://doi.org/10.1108/SR-06-2015-0098

Publisher

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Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited