M. Aswini, Gude Tejaswani, Maddi Sahithi, Boddu Hema Harshitha, Bellamkonda Mohana Vyshnavi | International Journal of Microwave Engineering and Technology | Vol 10, Issue 02 | pp. 41-50 | ISSN: 2455-0337
Abstract
Understanding human conduct requires the ability to recognize facial emotions, which has applications in everything from human-computer interaction to psychological wellness monitoring. This research provides a new approach to stress detection using Convolutional Neural Networks (or CNNs) and Haar Cascade classifiers. The suggested method uses CNN to recognize facial expressions and Haar Cascade algorithm for face detection. The methodology begins with preliminary processing the input photos, followed by face detection and extraction of facial regions. Those parts are then fed into the CNN model, which classifies emotions. The system has been trained and tested on publicly available datasets, with encouraging results in stress detection accuracy. This method, which detects stress through facial expressions, has potential uses in stress management, mental health evaluation, and personalized therapies.
Keywords: Convolutional Neural Networks, Haar Cascade classifier, Emotion transmission, Face recognition, Signal Processing, Stress detection, Real-time facial expression.
Keywords
Signal Processing, Face recognition, Convolutional Neural Networks, Haar Cascade classifier, Stress detection, Emotion transmission
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How to cite this article
@article{AswiniM2024,
author = {M. Aswini and Gude Tejaswani and Maddi Sahithi and Boddu Hema Harshitha and Bellamkonda Mohana Vyshnavi},
title = {Facial Emotion Based Stress Detection Using CNN and Haar Cascade Algorithms},
journal = {International Journal of Microwave Engineering and Technology},
year = {2024},
volume = {10},
number = {02},
pages = {41--50},
issn = {2455-0337},
url = {https://journalspub.com/publication/ijmet/article=12320}
}