TY - GEN
T1 - Eye Movement Classification with Temporal Convolutional Networks
AU - Elmadjian, Carlos
AU - Gonzales, Candy
AU - Morimoto, Carlos H.
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Recently, deep learning approaches have been proposed to detect eye movements such as fixations, saccades, and smooth pursuits from eye tracking data. These are end-to-end methods that have shown to surpass traditional ones, requiring no 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.
AB - Recently, deep learning approaches have been proposed to detect eye movements such as fixations, saccades, and smooth pursuits from eye tracking data. These are end-to-end methods that have shown to surpass traditional ones, requiring no 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.
KW - Eye movement classification
KW - Feature selection
KW - Temporal convolutional networks
UR - https://www.scopus.com/pages/publications/85104349808
U2 - 10.1007/978-3-030-68796-0_28
DO - 10.1007/978-3-030-68796-0_28
M3 - Conference contribution
AN - SCOPUS:85104349808
SN - 9783030687953
T3 - Lecture Notes in Computer Science
SP - 390
EP - 404
BT - Pattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings
A2 - Del Bimbo, Alberto
A2 - Cucchiara, Rita
A2 - Sclaroff, Stan
A2 - Farinella, Giovanni Maria
A2 - Mei, Tao
A2 - Bertini, Marco
A2 - Escalante, Hugo Jair
A2 - Vezzani, Roberto
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Pattern Recognition Workshops, ICPR 2021
Y2 - 10 January 2021 through 15 January 2021
ER -