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Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine.

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This study introduces a novel instructor facial expression recognition system to analyze teacher emotions in classrooms. The approach uses deep learning and a regularized extreme learning machine (RELM) for accurate emotion detection, enhancing the learning environment.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Educational Technology

Background:

  • Classroom communication research often focuses on student responses, neglecting the impact of instructor facial expressions.
  • Manual assessment of instructor behavior is time-consuming and resource-intensive.
  • Facial expression recognition can offer objective insights into teacher emotions and their effect on learning.

Purpose of the Study:

  • To develop and evaluate an automated system for recognizing instructor facial expressions in a classroom setting.
  • To improve the learning environment through intelligent assessment of teacher behavior.
  • To provide an efficient alternative to manual assessment strategies.

Main Methods:

  • A feedforward learning model was proposed for instructor facial expression recognition.
  • Face detection and key frame selection were performed on lecture videos.
  • Deep features were extracted using convolution neural networks and classified with a regularized extreme learning machine (RELM).
  • Experiments utilized a new classroom-based dataset and benchmark datasets (Cohn-Kanade, JAFFE, FER2013).

Main Results:

  • The proposed method demonstrated significant performance gains in accuracy, F1-score, and recall.
  • The system effectively classified five distinct instructor facial expressions.
  • Performance was validated against state-of-the-art techniques and traditional classifiers.

Conclusions:

  • Automated instructor facial expression recognition is feasible and effective for enhancing classroom environments.
  • The RELM classifier provides fast learning and good generalization for this task.
  • This technology has the potential to improve educational strategies and resource allocation.