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Recent advances in deep learning enhance automatic facial emotion recognition (FER). Combining deep learning models with architectural methods improves FER accuracy for better human-machine interaction and emotional artificial intelligence (EAI).

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Area of Science:

  • Computational intelligence
  • Artificial intelligence
  • Human-computer interaction

Background:

  • Facial emotion recognition (FER) is crucial for improving human-machine interactions.
  • Emotional artificial intelligence (EAI) aims to enhance computers' ability to analyze human emotions.
  • Deep learning advancements have significantly impacted FER research.

Purpose of the Study:

  • To review the latest advances in automated emotion recognition using deep learning models.
  • To explore the synergy between deep learning-based FER and architectural methods.

Main Methods:

  • Utilizing recent deep learning models for automated emotion recognition.
  • Integrating architectural methods, including the use of databases, with deep learning approaches.

Main Results:

  • Deep learning models show significant progress in FER.
  • The collaboration between deep learning-based FER and architectural methods yields highly accurate results.

Conclusions:

  • Deep learning is a key driver in the advancement of automated facial emotion recognition.
  • Combining diverse methodologies enhances the accuracy and effectiveness of FER systems.