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Eye centre localisation with convolutional neural network based regression

Zhang, Wenhao; Smith, Melvyn

Authors

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

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



Abstract

This paper introduces convolutional neural network regression models based on the Inception-v3 and the DenseNet architectures for accurate and real-time eye centre localisation. At a normalised error of e < 0.05, the proposed method yields an accuracy of 98.55% on the BioID dataset in a five-fold cross validation test, and 98.50% on the GI4E dataset in a cross-dataset validation test, outperforming the state-of-the-art methods. Both models, capable of running at 44 frames per second, demonstrate an excellent real-time performance. Not only is the proposed method highly accurate and efficient, it does not require invasive and expensive hardware, offering the potential for spawning applications in a wide variety of domains.

Citation

Zhang, W., & Smith, M. (2020). Eye centre localisation with convolutional neural network based regression. https://doi.org/10.1109/ICIVC47709.2019.8980972

Conference Name 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC)
Start Date Jul 5, 2019
End Date Jul 7, 2019
Acceptance Date Jun 14, 2019
Online Publication Date Feb 6, 2020
Publication Date Feb 6, 2020
Deposit Date Jul 29, 2019
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Pages 88-92
ISBN 9781728123257
DOI https://doi.org/10.1109/ICIVC47709.2019.8980972
Keywords eye centre localisation; eye tracking; convolutional neural network; linear regression
Public URL https://uwe-repository.worktribe.com/output/1787696