Authors :
Gowtham. J.; Hariprasanth. T; Janaki. V; Kaviya.S; Sindhuri.P
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/yp6k6sda
Scribd :
https://tinyurl.com/ms2997nn
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR2524
Abstract :
This project presents an innovative approach
to stress detection by utilizing Convolutional Neural
Networks (CNNs) to analyze emotional cues extracted
from facial images. The proposed system employs CNNs,
a class of deep learning models known for their efficacy in
image recognition tasks, to automatically extract features
from facial images. Through a combination of
convolutional, pooling, and fully connected layers, the
CNN learns hierarchical representations of facial
expressions associated with various emotions, including
those indicative of stress. The model is trained on a
diverse dataset encompassing a wide range of facial
expressions, allowing it to generalize well to unseen data.
Transfer learning techniques may also be employed to
leverage pre-trained CNN models, further enhancing
performance with limited data.
Keywords :
Facial Expressions Analysis, Emotional Recognition, Stress Level Indication.
References :
- C. Sharma and P. Saxena, “Stress Analysis for Students in Online Classess,” 2021.
- L. Liakopoulos, N. Stagakis, E. I. Zacharaki and K. Moustakas, “CNN- based stress and emotion recognition in ambulatory settings,” 2021.
- Bannore, T. Gore, A. Raut and K. Talele, “Mental stress detection using Machine Learning Algorithm,” 2021.
- Mulay, A. Dhekne, R. Wani, S. Kadam, P. Deshpande and P. Deshpande, "Automatic Depression Level Detection Through Visual Input," 2020.
- P. Kharel, K. Sharma, S. Dhimal and S. Sharma, "Early Detection of Depression and Treatment Response Prediction using Machine Learning: A Review," 2019.
- Figueiredo G. R., Ripka W. L., Romaneli, E. F. R. and Ulbricht L.,”Attentional bias for emotional faces in depressed and non-depressed Individuals: an eyetracking study”,2019.
- Anastasia Pampouchidou, Panagiotis Simos, Kostas Marias, Fabrice Meriaudeau, Fan Yang, Matthew Pediaditis, and Manolis Tsiknakis, “Automatic Assessment of Depression Based on Visual Cues: A Systematic Review”, IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2019.
This project presents an innovative approach
to stress detection by utilizing Convolutional Neural
Networks (CNNs) to analyze emotional cues extracted
from facial images. The proposed system employs CNNs,
a class of deep learning models known for their efficacy in
image recognition tasks, to automatically extract features
from facial images. Through a combination of
convolutional, pooling, and fully connected layers, the
CNN learns hierarchical representations of facial
expressions associated with various emotions, including
those indicative of stress. The model is trained on a
diverse dataset encompassing a wide range of facial
expressions, allowing it to generalize well to unseen data.
Transfer learning techniques may also be employed to
leverage pre-trained CNN models, further enhancing
performance with limited data.
Keywords :
Facial Expressions Analysis, Emotional Recognition, Stress Level Indication.
