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Related Experiment Video

Updated: Oct 23, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Gaze Gesture Recognition by Graph Convolutional Networks.

Lei Shi1, Cosmin Copot1, Steve Vanlanduit1

  • 1InViLab, Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium.

Frontiers in Robotics and AI
|August 23, 2021
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Summary
This summary is machine-generated.

This study introduces a graph convolutional network (GCN) for gaze gesture recognition, outperforming traditional methods. The GCN model achieved high accuracy, precision, and recall for hands-free human-computer interaction.

Keywords:
eye trackinggazegesture recognitiongraph convolution networkgraph neural network

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Area of Science:

  • Human-Computer Interaction
  • Machine Learning
  • Computer Vision

Background:

  • Gaze gestures are crucial for hands-free interaction with agents, computers, and robots.
  • Machine learning has shown success in gaze gesture recognition, with recent interest in graph neural networks (GNNs).
  • GNNs have been underutilized in eye-tracking research.

Purpose of the Study:

  • To propose and evaluate a novel graph convolutional network (GCN) model for gaze gesture recognition.
  • To compare the performance of the GCN model against conventional machine learning algorithms.

Main Methods:

  • Development of a GCN-based model for processing eye-tracking data.
  • Training and evaluation of the GCN model using the HideMyGaze! dataset.
  • Comparison with artificial neural network (ANN) and convolutional neural network (CNN) models.

Main Results:

  • The GCN model achieved high performance metrics: 97.62% accuracy, 97.18% precision, and 98.46% recall.
  • The GCN model outperformed ANN and CNN algorithms in gaze gesture recognition.
  • Demonstrated the effectiveness of GCNs in the domain of eye-tracking research.

Conclusions:

  • The proposed GCN model is highly effective for gaze gesture recognition.
  • GCNs offer superior performance compared to traditional ML algorithms for this task.
  • This research highlights the potential of GNNs to advance eye-tracking applications.