Skip to main content

Research Repository

See what's under the surface

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

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



Contributors

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.

Start Date Jul 5, 2019
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Institution Citation Zhang, W., & Smith, M. (in press). Eye Centre Localisation with Convolutional Neural Network Based Regression
Keywords eye centre localisation; eye tracking; convolutional neural network; linear regression