 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: March 2026
Volume 11 | Issue 3
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21686
Article Published : 8549
Total Authors : 22487
Total Reviewer : 811
Total Countries : 159
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Analysis of Facial Expressions to Estimate the Level of Engagement in Online Lectures |
| Authors Name: | K.Kavin , Mrs.S.Gowtham , S.Dharvesh Mushraf , N.Arun prakash , S.Sridhar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_203268
|
| Published Paper Id: | IJRTI2505003 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i5.203268 |
| Abstract: | Recognizing and enhancing student engagement is crucial for improving learning outcomes, particularly in the context of online classes where monitoring can be challenging. Traditional methods of attendance tracking, such as calling out names, are impractical and susceptible to manipulation in the virtual environment. This project is to develop a novel design using the AI based FFCNN (Face Fiducial Convolution Neural Network) model to capture face biometric randomly from students’ video stream and record their attendance automatically. In the proposed system, the learner’s face is monitored by a video camera while attending a video lecture using light Gradient Boosting Machine (LightGBM) to predict the attention and engagement level of the student by facial feature behavior analysis and random QA system. The present study aimed to develop a method for estimating students’ attentional state from facial expressions during online lectures. We estimated the level of attention while students watched a video lecture by measuring reaction time (RT) to detect a target sound that was irrelevant to the lecture. We assumed that RT to such a stimulus would be longer when participants were focusing on the lecture compared with when they were not. We sought to estimate how much learners focus on a lecture using RT measurement. In the experiment, the learner’s face was recorded by a video camera while watching a video lecture. Facial features were analyzed to predict RT to a task-irrelevant stimulus, which was assumed to be an index of the level of attention. We applied a machine learning method, light Gradient Boosting Machine (LightGBM), to estimate RTs from facial features extracted as action units (AUs) corresponding to facial muscle movements by an open-source software (OpenFace). The model obtained using LightGBM indicated that RTs to the irrelevant stimuli can be estimated from AUs, suggesting that facial expressions are useful for predicting attentional states while watching lectures. We re-analyzed the data while excluding RT data with sleepy faces of the students to test whether decreased general arousal caused by sleepiness was a significant factor in the RT lengthening observed in the experiment. The results were similar regardless of the inclusion of RTs with sleepy faces, indicating that facial expression can be used to predict learners’ level of attention to video lectures. |
| Keywords: | |
| Cite Article: | "Analysis of Facial Expressions to Estimate the Level of Engagement in Online Lectures", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a18-a23, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505003.pdf |
| Downloads: | 000467 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2505003
Registration ID:203268
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i5.203268
Page No: a18-a23 Country: tiruppur, Tamil Nadu, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505003 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505003 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||