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Real-Time Face Gesture-Based Robot Control Using GhostNet in a Unity Simulation Environment.

Yaseen1

  • 1Department of Electronics Engineering, Sejong University, Seoul 05006, Republic of Korea.

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This study introduces a novel facial gesture recognition system for contactless control of autonomous systems. The GhostNet-BiLSTM-Attention (GBA) method achieves 99.13% accuracy, enabling intuitive human-robot interaction.

Keywords:
GhostNet-BiLSTM-AttentionUnity simulationhuman–robot interactionreal-time recognitiontemporal facial gesture recognitiontouch-less control

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Traditional control systems rely on physical devices, limiting contactless interaction.
  • Advances in AI and computer vision enable facial gesture recognition for human-robot interaction.

Purpose of the Study:

  • To develop a lightweight, real-time facial gesture recognition system for operating autonomous systems.
  • To integrate the system with a 3D robot simulation for immersive control.

Main Methods:

  • Proposed a GhostNet-BiLSTM-Attention (GBA) model for facial gesture recognition.
  • Trained the GBA model on the FaceGest dataset.
  • Integrated the system with a Unity 3D robot simulation via socket communication.

Main Results:

  • Achieved 99.13% classification accuracy on the FaceGest dataset.
  • Demonstrated high accuracy and low inference latency in real-time evaluations.
  • Showcased robustness under varied user and lighting conditions.

Conclusions:

  • The GBA system offers an accurate and efficient method for contactless robot control.
  • Facial gesture recognition provides an intuitive interface for human-robot interaction.
  • Potential applications include assistive robotics, teleoperation, and immersive interfaces.