Abstract
Emotions play a vital role in education. Technological advancement in computer vision using deep learning models has improved automatic emotion recognition. In this study, a real-time automatic emotion recognition system is developed incorporating novel salient facial features for classroom assessment using a deep learning model. The proposed novel facial features for each emotion are initially detected using HOG for face recognition, and automatic emotion recognition is then performed by training a convolutional neural network (CNN) that takes real-time input from a camera deployed in the classroom. The proposed emotion recognition system will analyze the facial expressions of each student during learning. The selected emotional states are happiness, sadness, and fear along with the cognitive–emotional states of satisfaction, dissatisfaction, and concentration. The selected emotional states are tested against selected variables gender, department, lecture time, seating positions, and the difficulty of a subject. The proposed system contributes to improve classroom learning.
Funder
VSB—the Technical University of Ostrava
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference88 articles.
1. Jeffrey, C. (2006, January 3). Foundations of Human Computing Facial Expression and Emotion. Proceedings of the ICMI 2006 and IJCAI 2007 International Workshops, Banff, AB, Canada.
2. What and where are the primary affects? Some evidence for a theory;Tomkins;Am. Psychol. Assoc.,1964
3. Universals and cultural differences in the judgments of facial expressions of emotion;Ekman;J. Personal. Soc. Psychol.,1987
4. We Feel, Therefore We Learn: The Relevance of Affective and Social Neuroscience to Education;Damasio;Int. Mind Brain Educ. Soc.,2007
5. Zembyl, M., and Schutz, P.A. (2016). Methodological Advances in Research on Emotion and Education, Springer.
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