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Gaze Gesture Recognition by Graph Convolutional Networks.

Authors :
Shi L
Copot C
Vanlanduit S
Source :
Frontiers in robotics and AI [Front Robot AI] 2021 Aug 05; Vol. 8, pp. 709952. Date of Electronic Publication: 2021 Aug 05 (Print Publication: 2021).
Publication Year :
2021

Abstract

Gaze gestures are extensively used in the interactions with agents/computers/robots. Either remote eye tracking devices or head-mounted devices (HMDs) have the advantage of hands-free during the interaction. Previous studies have demonstrated the success of applying machine learning techniques for gaze gesture recognition. More recently, graph neural networks (GNNs) have shown great potential applications in several research areas such as image classification, action recognition, and text classification. However, GNNs are less applied in eye tracking researches. In this work, we propose a graph convolutional network (GCN)-based model for gaze gesture recognition. We train and evaluate the GCN model on the HideMyGaze! dataset. The results show that the accuracy, precision, and recall of the GCN model are 97.62%, 97.18%, and 98.46%, respectively, which are higher than the other compared conventional machine learning algorithms, the artificial neural network (ANN) and the convolutional neural network (CNN).<br />Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.<br /> (Copyright © 2021 Shi, Copot and Vanlanduit.)

Details

Language :
English
ISSN :
2296-9144
Volume :
8
Database :
MEDLINE
Journal :
Frontiers in robotics and AI
Publication Type :
Academic Journal
Accession number :
34422914
Full Text :
https://doi.org/10.3389/frobt.2021.709952