Recently, deep learning approaches have been proposed to detect eye movements such as fixations, saccades, and smooth pursuits from eye tracking data. These are textit{end-to-end} methods that have shown to surpass traditional ones, requiring no textit{ad hoc} parameters. In this work we propose the use of temporal convolutional networks (TCNs) for automated eye movement classification and investigate the influence of feature space, scale, and context window sizes on the classification results.
We evaluated the performance of TCNs against a state-of-the-art 1D-CNN-BLSTM model using GazeCom, a public available dataset.
Our results show that TCNs can outperform the 1D-CNN-BLSTM, achieving an F-score of 94.2% for fixations, 89.9% for saccades, and 73.7% for smooth pursuits on sample level, and 89.6%, 94.3%, and 60.2% on event level. We also state the advantages of TCNs over sequential networks for this problem, and how these scores can be further improved by feature space extension.
keywords{Eye movement classification and Temporal convolutional networks and Feature selection.
BibTeX
@inproceedings{Elmadjian:2020,
author = {Elmadjian, Carlos and Gonzales, Candy and Morimoto, Carlos H.},
title = {Eye Movement Classification With Temporal Convolutional Networks},
booktitle = {Proceedings of the Workshop on Eye Tracking Techniques, Applications and Challenges},
series = {ETTAC '20},
year = {2020},
url = {https://vision.unipv.it/ettac2020/program.html},
}