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Related Experiment Video

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Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
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Gaze Estimation Network Based on Multi-Head Attention, Fusion, and Interaction.

Changli Li1, Fangfang Li1, Kao Zhang1

  • 1School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved gaze estimation network that effectively fuses eye and facial features using multi-head attention. The novel approach enhances accuracy in determining visual attention by better utilizing feature correlations.

Keywords:
convolutional neural networkfeature interactiongaze estimationmulti-head attention

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Gaze position is a key indicator of human visual attention, crucial for various applications.
  • Current gaze estimation models often process eye and facial data separately, limiting the exploitation of feature correlations.

Purpose of the Study:

  • To develop a novel gaze estimation network that integrates facial and eye features more effectively.
  • To improve the accuracy and robustness of gaze estimation by leveraging feature correlations.

Main Methods:

  • Proposed a new gaze estimation network integrating multi-head attention, fusion, and interaction strategies.
  • Employed multi-head attention and channel attention to fuse features from both eyes.
  • Introduced a face and eye interaction module and modified the Convolutional Block Attention Module (CBAM) with minimum pooling and shortcut connections.

Main Results:

  • The proposed network demonstrated superior performance compared to existing methods.
  • Experiments on Gaze360, MPIIFaceGaze, and EYEDIAP datasets validated the method's effectiveness.
  • The integration of facial and eye features significantly improved gaze estimation accuracy.

Conclusions:

  • The developed gaze estimation network effectively fuses multi-modal features, outperforming previous approaches.
  • The novel attention and interaction mechanisms enhance the model's ability to capture subtle gaze cues.
  • This work offers a significant advancement in accurate and reliable gaze estimation technology.