Abstract
In non-verbal communication, facial emotions play a very crucial role. Facial recognition can be useful in various ways, such as understanding people better and using the collected data in various fields. In an e-learning platform, students’ facial expressions determine their comprehension levels. Students’ facial emotions can have a favorable or unfavorable impact on their academic performance. As a result, instructors need to create a positive, emotionally secure classroom environment to optimize student learning. In this paper, a novel Facial Emotion Recognition for improving our understanding of students during e-learning is proposed. Suggested model detects different students’ facial emotions such as anger, disgust, fear, happiness, sadness, surprise, and neutral and utilizing them for better teaching and learning during a lecture in an e-learning platform. Convolutional neural networks (CNNs) have been used for detecting facial emotions of students in e-learning platforms, and the proposed model shows an outcome of test accuracy of 67.5%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hassouneh A, Mutawa AM, Murugappan M (2020) Development of a real-time emotion recognition system using facial expressions and EEG based on machine learning and deep neural network methods. Inform Med Unlocked 20:100372
Mega C, Ronconi L, De Beni R (2014) What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. J Educ Psychol 106(1):121
El Hammoumi O, Benmarrakchi F, Ouherrou N, El Kafi J, El Hore A (2018, May) Emotion recognition in e-learning systems. In: 2018 6th International conference on multimedia computing and systems (ICMCS). IEEE, pp 1–6
Ch S (2021) An efficient facial emotion recognition system using novel deep learning neural network-regression activation classifier. Multimedia Tools Appl 80(12):17543–17568
Hajarolasvadi N, Bashirov E, Demirel H (2021) Video-based person-dependent and person-independent facial emotion recognition. Signal Image Video Process 15(5):1049–1056
Mansouri-Benssassi E, Ye J (2021) Generalisation and robustness investigation for facial and speech emotion recognition using bio-inspired spiking neural networks. Soft Comput 25(3):1717–1730
Saravanan A, Perichetla G, Gayathri DK (2019) Facial emotion recognition using convolutional neural networks. arXiv:1910.05602
Mehendale N (2020) Facial emotion recognition using convolutional neural networks (FERC). SN Appl Sci 2(3):1–8
Rzayeva Z, Alasgarov E (2019, October) Facial emotion recognition using convolutional neural networks. In 2019 IEEE 13th international conference on application of information and communication technologies (AICT). IEEE, pp 1–5
Zadeh MMT, Imani M, Majidi B (2019, Feb) Fast facial emotion recognition using convolutional neural networks and Gabor filters. In: 2019 5th Conference on knowledge based engineering and innovation (KBEI). IEEE, pp 577–581
Chen JX, Zhang PW, Mao ZJ, Huang YF, Jiang DM, Zhang YN (2019) Accurate EEG-based emotion recognition on combined features using deep convolutional neural networks. IEEE Access 7:44317–44328
Pranav E, Kamal S, Chandran CS, Supriya MH (2020, Mar) Facial emotion recognition using deep convolutional neural network. In: 2020 6th International conference on advanced computing and communication systems (ICACCS). IEEE, pp 317–320
Thuseethan S, Rajasegarar S, Yearwood J (2019, July) Emotion intensity estimation from video frames using deep hybrid convolutional neural networks. In: 2019 International joint conference on neural networks (IJCNN). IEEE, pp 1–10
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mukherjee, S., Suhakar, B.Y., Kamma, S., Barukula, S., Agarwal, P., Singh, P. (2023). Automated Student Emotion Analysis During Online Classes Using Convolutional Neural Network. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_2
Download citation
DOI: https://doi.org/10.1007/978-981-19-6525-8_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6524-1
Online ISBN: 978-981-19-6525-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)