Abstract
Many sectors have experienced the impact of the COVID-19 outbreak, without exception education. The method of process learning transformed from face-to-face meeting learning became online learning. Learners tried to adapt to this unexpected circumstance. In the online learning approach, the instructors only assumed the degree of learners’ understanding with their face emotion expressions spontaneously. Advancement technology enables the machine to learn data fast and accurately. Mostly, the position of the learner’s face in front of the camera when attending the online course, and the DLIB’s shape detector model map the landmark of the captured face. Deep learning is a subset domain of machine learning. Convolutional Neural Network (CNN) model as a deep learning approach has characteristics in the high computation and ease of implementation. The work proposed a face-emotion expression recognition model for supporting online learning. The combination ratio images dataset was 80% data training and 20% data testing, and the condition expression was determined with a deep learning approach. The experimental results showed that the recognition accuracy of the proposed model achieved 97% for dataset image input.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Survey on face detection algorithms. Int. J. Innov. Sci. Res. Technol. 6 (2021)
Bezerra, G.A., Gomes, R.B.: Recognition of occluded and lateral faces using MTCNN, DLIB and homographies, 11 (2018)
Kamal, K.C., et al.: Impacts of background removal on convolutional neural networks for plant disease classification in-situ (2021)
Krstinić, D., Braović, M., Šerić, L., Božić-Štulić, D.: Multi-label classifier performance evaluation with confusion matrix, pp. 01–14. Academy and Industry Research Collaboration Center (AIRCC) (2020)
Mohanty, S., Hegde, S.V., Prasad, S., Manikandan, J.: Design of real-time drowsiness detection system using DLIB (2019)
Mukhopadhyay, M., Pal, S., Nayyar, A., Pramanik, P.K.D., Dasgupta, N., Choudhury, P.: Facial emotion detection to assess learner’s state of mind in an online learning system. In: ACM International Conference Proceeding Series, pp. 107–115 (2020)
Tarnowski, P., Kołodziej, M., Majkowski, A., Rak, R.J.: Emotion recognition using facial expressions. Procedia Comput. Sci. 108, 1175–1184 (2017)
Acknowledgement
This research was supported in part by the National Science and Technology Council (NSTC), Taiwan R.O.C. grants numbers 111-2622-E-029-003, 111-2811-E-029-001, 111-2621-M-029-004, and 110-2221-E-029-020-MY3.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wiryasaputra, R., Huang, CY., Juliansyah, J., Yang, CT. (2023). Face Emotion Expression Recognition Using DLIB Model and Convolutional Neural Network Approach for Supporting Online Learning. In: Deng, DJ., Chao, HC., Chen, JC. (eds) Smart Grid and Internet of Things. SGIoT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-031-31275-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-31275-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31274-8
Online ISBN: 978-3-031-31275-5
eBook Packages: Computer ScienceComputer Science (R0)