LEARNERS' EMOTIONS ESTIMATION USING VIDEO PROCESSING TECHNIQUES FOR OPTIMUM E-LEARNING EXPERIENCE

Authors

  • Mohammed Subhi Ministry of Higher Education and Scientific Research

DOI:

https://doi.org/10.52866/ijcsm.2024.05.03.038

Keywords:

Learning Management System (LMS), E-learning, Emotional Recognition System (ERS), Convolutional Neural Network (CNN), Intelligent Recognition System (IRS), Mel-Frequency Cepstral Coefficients (MFCC), Linear Predictive Cepstral Coefficients (LPCC).

Abstract

 Learning management systems (LMSs) have integrated multiple technologies to enhance the elearning experience. One such technology is the emotional recognition system (ERS), which provides tutors with
data on learners' emotions, including anger, sadness, happiness, and more. ERS utilizes various data sources like
facial expressions, body activities, and brain signals to recognize emotions. This paper provides an overview of the
ERS structure and discusses the state-of-the-art technologies in this field. The results indicate that deep learning
based ERS using VGG19 for feature extraction over the FER2013 dataset is reliable with a recognition accuracy of
87% using Random Forest Algorithm.

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Published

2024-08-16

How to Cite

[1]
M. Subhi, “LEARNERS’ EMOTIONS ESTIMATION USING VIDEO PROCESSING TECHNIQUES FOR OPTIMUM E-LEARNING EXPERIENCE”, Iraqi Journal For Computer Science and Mathematics, vol. 5, no. 3, Aug. 2024.
CITATION
DOI: 10.52866/ijcsm.2024.05.03.038
Published: 2024-08-16

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Section

Articles