Dr Wenhao Zhang Wenhao.Zhang@uwe.ac.uk
Associate Professor of Computer Vision and Machine Learning
Eye centre localisation with convolutional neural network based regression
Zhang, Wenhao; Smith, Melvyn
Authors
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) |
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Conference Location | Xiamen, China |
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 |
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