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

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.

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).