Skip to main content

Research Repository

Advanced Search

Eye centre localisation: An unsupervised modular approach

Zhang, Wenhao; Smith, Melvyn Lionel; Smith, Lyndon Neal; Farooq, Abdul Rehman

Authors

Wenhao Zhang Wenhao.Zhang@uwe.ac.uk
Senior Lecturer in Machine Vision

Profile Image

Melvyn Smith Melvyn.Smith@uwe.ac.uk
Research Centre Director Vision Lab/Prof

Lyndon Smith Lyndon.Smith@uwe.ac.uk
Professor in Computer Simulation and Machine

Abdul Farooq Abdul2.Farooq@uwe.ac.uk
Associate Head of Departmemt Business Engagement and Partnerships



Abstract

© Emerald Group Publishing Limited. 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.

Citation

Zhang, W., Smith, M. L., Smith, L. N., & Farooq, A. R. (2016). Eye centre localisation: An unsupervised modular approach. Sensor Review, 36(3), 277-286. https://doi.org/10.1108/SR-06-2015-0098

Journal Article Type Article
Acceptance Date Mar 6, 2016
Online Publication Date Jun 20, 2016
Publication Date Jun 20, 2016
Deposit Date Mar 14, 2016
Journal Sensor Review
Print ISSN 0260-2288
Electronic ISSN 0260-2288
Publisher Emerald
Peer Reviewed Peer Reviewed
Volume 36
Issue 3
Pages 277-286
DOI https://doi.org/10.1108/SR-06-2015-0098
Keywords Eyes; Pattern recognition; Eye centre localization; HCI; Pupil and iris analysis
Public URL https://uwe-repository.worktribe.com/output/924778
Publisher URL http://www.emeraldinsight.com/doi/abs/10.1108/SR-06-2015-0098
Additional Information Additional Information : The published version of this article is available at http://www.emeraldinsight.com/doi/abs/10.1108/SR-06-2015-0098

Files







You might also like



Downloadable Citations