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Updated: Jan 7, 2026

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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Temporal Capsule Feature Network for Eye-Tracking Emotion Recognition.

Qingfeng Gu1, Jiannan Chi1,2, Cong Zhang1

  • 1Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Brain Sciences
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new Temporal Capsule Feature Network (TCFN) to improve emotion recognition using eye tracking (ET) data. The TCFN enhances temporal dynamics and feature specificity, achieving high accuracy in emotion classification tasks.

Keywords:
MLP classificationcapsule networkemotion recognitioneye trackingtemporal feature network

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Area of Science:

  • Physiological signal analysis
  • Affective computing
  • Human-computer interaction

Background:

  • Eye tracking (ET) parameters are valuable physiological signals for emotion recognition.
  • Current methods struggle with extracting temporal dynamics and emotionally specific features from ET data.
  • Challenges include limited model robustness and individual generalization capabilities.

Purpose of the Study:

  • To propose a novel Temporal Capsule Feature Network (TCFN) for enhanced eye tracking-based emotion recognition.
  • To address limitations in temporal dynamic information extraction and feature specificity in existing models.
  • To improve the robustness and individual generalization of emotion recognition systems.

Main Methods:

  • Developed a Temporal Capsule Feature Network (TCFN) incorporating a Window Feature Module for temporal dynamics.
  • Utilized a specialized Capsule Network Module to capture feature interdependencies.
  • Implemented an MLP Classification Module and a Dual-Loss Mechanism for optimized performance.

Main Results:

  • Achieved 83.27% average accuracy for Arousal and 89.94% for Valence (three-class) on the eSEE-d dataset.
  • Reached 63.85% accuracy for four-category, across-session emotion recognition on the SEED-IV dataset.
  • Demonstrated the superiority of the TCFN model in emotion recognition tasks.

Conclusions:

  • The proposed TCFN effectively extracts temporal dynamic information and emotionally specific features from eye tracking data.
  • TCFN shows significant improvements in accuracy and generalization for emotion recognition.
  • This approach offers a promising direction for advanced affective computing systems.