E3S Web Conf.
Volume 351, 202210th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|Number of page(s)||6|
|Published online||24 May 2022|
Using Deep Learning to detect Facial Expression from front camera: Towards students’ interactions analyze
1 TIMS team, Abdelmalek Essadi University, Tetuan, Morocco
2 SED Laboratory, Private University of Fez, Fez, Morocco
3 ICT Team, Hamad Bin Khalifa University, Doha, Qatar
* Corresponding author: email@example.com
The recent advancement of Artificial Intelligence (AI) affords ambition to exploit this revolution in multiple fields. Computer-assisted teaching and learning creates a very important area of AI application. Consequently, this last will be able to revolutionize this field. In research conducted by our laboratory, we are interested to explore AI trends to teaching and learning technologies. As part of this, we aim to study learner’s behaviors in education and learning environment, thus we aim to analyze the student through the front camera, as a first step we intend to develop a model that classify face’s images based on deep learning and Convolutional Neural Networks (CNN) in particular. Model development of images classification can be realized based in several technologies, we have chosen for this study to use IBM solutions, which are provided on the cloud. This paper describes the training experiment and the model development based on two alternatives proposed by IBM where the goal is to generate the most precise model. It presents a comparative study between the two approaches and ends with result discussing and the choice of the accurate solution for deployment in our teaching and learning system.
Key words: Deep Learning / Artificial Neural Network / Computer Vision / Emotion Detection
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.