Diverse distant-students deep emotion recognition and visualization

Ala'a Harb; Abdalla Gad; Maha Yaghi; Marah Alhalabi; Huma Zia; Jawad Yousaf; Adel Khelifi; Kilani Ghoudi; Mohammed Ghazal

doi:10.1016/j.compeleceng.2023.108963

Diverse distant-students deep emotion recognition and visualization

Ala'a Harb, Abdalla Gad, Maha Yaghi, Marah Alhalabi, Huma Zia, Jawad Yousaf, Adel Khelifi, Kilani Ghoudi, Mohammed Ghazal

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

Distance learning through online platforms has recently emerged as a significant addition to the educational system, providing support and enhancing traditional delivery modes in schools. Instructors need visual feedback to monitor students’ engagement. This research aims to investigate the emotional state of culturally diverse students in the country throughout the lecture and provide teachers with an interactive dashboard to monitor students’ emotional states during a particular lecture. We collected a specialized dataset that includes Arab students posing for various emotions specifically related to lecture scenarios. Face detection and alignment algorithm is then applied. Finally, we utilize Neural Architecture Search (NAS) to optimize emotion classification architecture. The overall accuracy of the model is 86.29% on our collected dataset and 98.84% on the CK+ dataset. The live testing of the system shows the emotions clearly with real-time feedback. The AI-powered dashboard gives instructors insights into student engagement during distance learning, which induces data-driven decisions to optimize teaching strategies.

Original language	English
Article number	108963
Journal	Computers and Electrical Engineering
Volume	111
DOIs	https://doi.org/10.1016/j.compeleceng.2023.108963
Publication status	Published - Nov 2023

Keywords

Computer vision
Data augmentation
Deep learning
Face alignment
Facial emotion recognition
Neural Architecture Search (NAS)

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science
Electrical and Electronic Engineering

Access to Document

10.1016/j.compeleceng.2023.108963

Cite this

@article{da2a53b75c4e487e9621ee70e9af46b1,

title = "Diverse distant-students deep emotion recognition and visualization",

abstract = "Distance learning through online platforms has recently emerged as a significant addition to the educational system, providing support and enhancing traditional delivery modes in schools. Instructors need visual feedback to monitor students{\textquoteright} engagement. This research aims to investigate the emotional state of culturally diverse students in the country throughout the lecture and provide teachers with an interactive dashboard to monitor students{\textquoteright} emotional states during a particular lecture. We collected a specialized dataset that includes Arab students posing for various emotions specifically related to lecture scenarios. Face detection and alignment algorithm is then applied. Finally, we utilize Neural Architecture Search (NAS) to optimize emotion classification architecture. The overall accuracy of the model is 86.29% on our collected dataset and 98.84% on the CK+ dataset. The live testing of the system shows the emotions clearly with real-time feedback. The AI-powered dashboard gives instructors insights into student engagement during distance learning, which induces data-driven decisions to optimize teaching strategies.",

keywords = "Computer vision, Data augmentation, Deep learning, Face alignment, Facial emotion recognition, Neural Architecture Search (NAS)",

author = "Ala'a Harb and Abdalla Gad and Maha Yaghi and Marah Alhalabi and Huma Zia and Jawad Yousaf and Adel Khelifi and Kilani Ghoudi and Mohammed Ghazal",

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = nov,

doi = "10.1016/j.compeleceng.2023.108963",

language = "English",

volume = "111",

journal = "Computers and Electrical Engineering",

issn = "0045-7906",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Diverse distant-students deep emotion recognition and visualization

AU - Harb, Ala'a

AU - Gad, Abdalla

AU - Yaghi, Maha

AU - Alhalabi, Marah

AU - Zia, Huma

AU - Yousaf, Jawad

AU - Khelifi, Adel

AU - Ghoudi, Kilani

AU - Ghazal, Mohammed

PY - 2023/11

Y1 - 2023/11

N2 - Distance learning through online platforms has recently emerged as a significant addition to the educational system, providing support and enhancing traditional delivery modes in schools. Instructors need visual feedback to monitor students’ engagement. This research aims to investigate the emotional state of culturally diverse students in the country throughout the lecture and provide teachers with an interactive dashboard to monitor students’ emotional states during a particular lecture. We collected a specialized dataset that includes Arab students posing for various emotions specifically related to lecture scenarios. Face detection and alignment algorithm is then applied. Finally, we utilize Neural Architecture Search (NAS) to optimize emotion classification architecture. The overall accuracy of the model is 86.29% on our collected dataset and 98.84% on the CK+ dataset. The live testing of the system shows the emotions clearly with real-time feedback. The AI-powered dashboard gives instructors insights into student engagement during distance learning, which induces data-driven decisions to optimize teaching strategies.

AB - Distance learning through online platforms has recently emerged as a significant addition to the educational system, providing support and enhancing traditional delivery modes in schools. Instructors need visual feedback to monitor students’ engagement. This research aims to investigate the emotional state of culturally diverse students in the country throughout the lecture and provide teachers with an interactive dashboard to monitor students’ emotional states during a particular lecture. We collected a specialized dataset that includes Arab students posing for various emotions specifically related to lecture scenarios. Face detection and alignment algorithm is then applied. Finally, we utilize Neural Architecture Search (NAS) to optimize emotion classification architecture. The overall accuracy of the model is 86.29% on our collected dataset and 98.84% on the CK+ dataset. The live testing of the system shows the emotions clearly with real-time feedback. The AI-powered dashboard gives instructors insights into student engagement during distance learning, which induces data-driven decisions to optimize teaching strategies.

KW - Computer vision

KW - Data augmentation

KW - Deep learning

KW - Face alignment

KW - Facial emotion recognition

KW - Neural Architecture Search (NAS)

UR - http://www.scopus.com/inward/record.url?scp=85173151332&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85173151332&partnerID=8YFLogxK

U2 - 10.1016/j.compeleceng.2023.108963

DO - 10.1016/j.compeleceng.2023.108963

M3 - Article

AN - SCOPUS:85173151332

SN - 0045-7906

VL - 111

JO - Computers and Electrical Engineering

JF - Computers and Electrical Engineering

M1 - 108963

ER -

Diverse distant-students deep emotion recognition and visualization

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this