Engagement Detection through Facial Emotional Recognition Using a Shallow Residual Convolutional Neural Networks
- Title
- Engagement Detection through Facial Emotional Recognition Using a Shallow Residual Convolutional Neural Networks
- Creator
- Thiruthuvanathan M.M.; Krishnan B.; Rangaswamy M.
- Description
- Online teaching and learning has recently turned out to be the order of the day, where majority of the learners undergo courses and trainings over the new environment. Learning through these platforms have created a requirement to understand if the learner is interested or not. Detecting engagement of the learners have sought increased attention to create learner centric models that can enhance the teaching and learning experience. The learner will over a period of time in the platform, tend to expose various emotions like engaged, bored, frustrated, confused, angry and other cues that can be classified as engaged or disengaged. This paper proposes in creating a Convolutional Neural Network (CNN) and enabling it with residual connections that can enhance the learning rate of the network and improve the classification on three Indian datasets that predominantly work on classroom engagement models. The proposed network performs well due to introduction of Residual learning that carries additional learning from the previous batch of layers into the next batch, Optimized Hyper Parametric (OHP) setting, increased dimensions of images for higher data abstraction and reduction of vanishing gradient problems resulting in managing overfitting issues. The Residual network introduced, consists of a shallow depth of 50 layers which has significantly produced an accuracy of 91.3% on ISED & iSAFE data while it achieves a 93.4% accuracy on the Daisee dataset. The average accuracy achieved by the classification network is 0.825 according to Cohens Kappa measure. 2020, Intelligent Engineering & System. All rights reserved.
- Source
- International Journal of Intelligent Engineering and Systems, Vol-14, No. 2, pp. 236-247.
- Date
- 2021-01-01
- Publisher
- Intelligent Network and Systems Society
- Subject
- Convolutional neural network; Emotion detection; Facial expression recognition.; Residual networks; Student engagement detection
- Coverage
- Thiruthuvanathan M.M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Rangaswamy M., Department of Psychology, School of Social Sciences, Bangalore, India
- Rights
- All Open Access; Bronze Open Access
- Relation
- ISSN: 2185310X
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Thiruthuvanathan M.M.; Krishnan B.; Rangaswamy M., “Engagement Detection through Facial Emotional Recognition Using a Shallow Residual Convolutional Neural Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 4, 2025, https://archives.christuniversity.in/items/show/16018.