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Comparative studies of facial emotion detection in online learning.

Due to the COVID-19 outbreak that hits everyone globally, every person is affected, including students. Starting with this, no one can refuse the importance of a smart online learning system being embedded in the education system anymore. Still, the emotional engagement between teachers and students...

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Main Authors: Ahmad, Asraful Syifaa', Hassan, Rohayanti, Zakaria, Noor Hidayah, Moi, Sim Hiew
Format: Conference or Workshop Item
Published: 2023
Subjects:
T58.5-58.64 Information technology
Online Access:http://eprints.utm.my/107389/
http://dx.doi.org/10.1063/5.0164746
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spelling my.utm.1073892024-09-11T04:34:27Z http://eprints.utm.my/107389/ Comparative studies of facial emotion detection in online learning. Ahmad, Asraful Syifaa' Hassan, Rohayanti Zakaria, Noor Hidayah Moi, Sim Hiew T58.5-58.64 Information technology Due to the COVID-19 outbreak that hits everyone globally, every person is affected, including students. Starting with this, no one can refuse the importance of a smart online learning system being embedded in the education system anymore. Still, the emotional engagement between teachers and students is in doubt. Teachers tend to teach without knowing whether the students understand it. It is due to the limitation of having an online class instead of a physical one, but can be overcome by embedding emotion detection using facial expressions during the course. However, the next challenge is to overcome the system's limitations. Even though people have a well-equipped tool for themselves in this situation, it is hard to determine the exact emotion of the students during the learning process. The facial image captured in the real world usually has multiple issues, including (1) an obstacle in front of the face, (2) a different distance between the head and camera, and (3) low-resolution images due to the low bandwidth, causing the low accuracy of emotion classification. Thus, this paper took the initiative to compare two different datasets tested on four classifiers: CNN, DCNN, Transfer Learning, and Multiple Pipeline. Four different algorithm sets were used to test two datasets: FER2013 and our dataset. The result shows that the data captured in a real-world situation using an independent device setting contains some issues with the slightly low accuracy of emotion classification and leads to false classification compared to FER2013 data. Thus, this can be improved by having a powerful emotion detection system that can capture all the issues in a real-world situation. 2023-09-12 Conference or Workshop Item PeerReviewed Ahmad, Asraful Syifaa' and Hassan, Rohayanti and Zakaria, Noor Hidayah and Moi, Sim Hiew (2023) Comparative studies of facial emotion detection in online learning. In: 11th International Conference on Applied Science and Technology 2022, ICAST 2022, 13 June 2022 - 14 June 2022, Putrajaya, Malaysia - Hybrid. http://dx.doi.org/10.1063/5.0164746
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T58.5-58.64 Information technology
spellingShingle T58.5-58.64 Information technology
Ahmad, Asraful Syifaa'
Hassan, Rohayanti
Zakaria, Noor Hidayah
Moi, Sim Hiew
Comparative studies of facial emotion detection in online learning.
description Due to the COVID-19 outbreak that hits everyone globally, every person is affected, including students. Starting with this, no one can refuse the importance of a smart online learning system being embedded in the education system anymore. Still, the emotional engagement between teachers and students is in doubt. Teachers tend to teach without knowing whether the students understand it. It is due to the limitation of having an online class instead of a physical one, but can be overcome by embedding emotion detection using facial expressions during the course. However, the next challenge is to overcome the system's limitations. Even though people have a well-equipped tool for themselves in this situation, it is hard to determine the exact emotion of the students during the learning process. The facial image captured in the real world usually has multiple issues, including (1) an obstacle in front of the face, (2) a different distance between the head and camera, and (3) low-resolution images due to the low bandwidth, causing the low accuracy of emotion classification. Thus, this paper took the initiative to compare two different datasets tested on four classifiers: CNN, DCNN, Transfer Learning, and Multiple Pipeline. Four different algorithm sets were used to test two datasets: FER2013 and our dataset. The result shows that the data captured in a real-world situation using an independent device setting contains some issues with the slightly low accuracy of emotion classification and leads to false classification compared to FER2013 data. Thus, this can be improved by having a powerful emotion detection system that can capture all the issues in a real-world situation.
format Conference or Workshop Item
author Ahmad, Asraful Syifaa'
Hassan, Rohayanti
Zakaria, Noor Hidayah
Moi, Sim Hiew
author_facet Ahmad, Asraful Syifaa'
Hassan, Rohayanti
Zakaria, Noor Hidayah
Moi, Sim Hiew
author_sort Ahmad, Asraful Syifaa'
title Comparative studies of facial emotion detection in online learning.
title_short Comparative studies of facial emotion detection in online learning.
title_full Comparative studies of facial emotion detection in online learning.
title_fullStr Comparative studies of facial emotion detection in online learning.
title_full_unstemmed Comparative studies of facial emotion detection in online learning.
title_sort comparative studies of facial emotion detection in online learning.
publishDate 2023
url http://eprints.utm.my/107389/
http://dx.doi.org/10.1063/5.0164746
_version_ 1811681173694316544
score 13.211869

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