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Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
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Person-Specific Gaze Estimation from Low-Quality Webcam Images.

Mohd Faizan Ansari1, Pawel Kasprowski1, Peter Peer2

  • 1Department of Applied Informatics, Silesian University of Technology, 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

Person-specific gaze estimation using convolutional neural networks (CNNs) achieves higher accuracy than generalized models. This method uses low-quality webcam images, making it accessible for various applications like human-computer interaction.

Keywords:
computer visionconvolution neural networkdeep learninggaze estimation

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Gaze estimation is crucial for applications in healthcare, virtual reality, and human-computer interaction.
  • Deep learning, particularly Convolutional Neural Networks (CNNs), has shown significant success in various computer vision tasks.
  • Recent advancements have focused on applying deep learning to gaze estimation.

Purpose of the Study:

  • To develop and evaluate a person-specific gaze estimation model using CNNs.
  • To assess the performance of person-specific models against generalized models.
  • To demonstrate the feasibility of using low-quality webcam images for accurate gaze estimation.

Main Methods:

  • A person-specific gaze estimation model was implemented using a CNN.
  • A dataset of face and eye images was collected using a standard desktop webcam.
  • Various CNN hyperparameters, including learning and dropout rates, were tested and optimized.

Main Results:

  • Person-specific models demonstrated superior performance compared to generalized models.
  • The best Mean Absolute Error (MAE) achieved was 30.09 for the whole face (1.14 degrees).
  • Specific MAE results were: left eye 38.20 (1.45 degrees), right eye 36.01 (1.37 degrees), and both eyes 51.18 (1.98 degrees).

Conclusions:

  • Person-specific gaze estimation models, when properly hyperparameter-tuned, yield better results than universal models.
  • The proposed method is effective even with low-quality images from standard webcams.
  • This approach offers a practical and hardware-independent solution for gaze estimation.