Non-Intrusive Gaze Tracking Using Artificial Neural Networks
Abstract
We have developed an artificial neural network based gaze tracking system which can be customized to individual users. Unlike other gaze trackers, which normally require the user to wear cumbersome headgear, or to use a chin rest to ensure head immobility, our system is entirely non-intrusive. Currently, the best intrusive gaze tracking systems are accurate to approxi(cid:173) mately 0.75 degrees. In our experiments, we have been able to achieve an accuracy of 1.5 degrees, while allowing head mobility. In this paper we present an empirical analysis of the performance of a large number of artifi(cid:173) cial neural network architectures for this task.
Cite
Text
Baluja and Pomerleau. "Non-Intrusive Gaze Tracking Using Artificial Neural Networks." Neural Information Processing Systems, 1993.Markdown
[Baluja and Pomerleau. "Non-Intrusive Gaze Tracking Using Artificial Neural Networks." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/baluja1993neurips-nonintrusive/)BibTeX
@inproceedings{baluja1993neurips-nonintrusive,
title = {{Non-Intrusive Gaze Tracking Using Artificial Neural Networks}},
author = {Baluja, Shumeet and Pomerleau, Dean},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {753-760},
url = {https://mlanthology.org/neurips/1993/baluja1993neurips-nonintrusive/}
}