AUTHOR=Shi Lei , Copot Cosmin , Vanlanduit Steve 

TITLE=Gaze Gesture Recognition by Graph Convolutional Networks

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2021.709952

DOI=10.3389/frobt.2021.709952

ISSN=2296-9144

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 researches have demonstrated the success of applying machine learning techniques for gaze gesture recognition. More recently, Graph Neural Networks (GNNs) have shown great potential in lots of research areas such as image classification, action recognition, text classification and so on. However, the GNNs are less applied in eye tracking related researches. In this work, we propose to use 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 GCN model has better accuracy than several conventional machine learning algorithms and Convolutional Neural Networks (CNNs).