Facial Emotion Based Stress Detection Using CNN and Haar Cascade Algorithms

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Microwave Engineering and Technology
Received Date: 10/26/2024
Acceptance Date: 11/06/2024
Published On: 2024-11-28
First Page: 41
Last Page: 50

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By: M. Aswini, Gude Tejaswani, Maddi Sahithi, Boddu Hema Harshitha, and Bellamkonda Mohana Vyshnavi

1. M. Aswini,Student, Department of Computer Science Engineering, Gayatri Vidya Parishad College of Engineering for women, Visakhapatnam, India
2.Gude Tejaswani,Student, Department of Computer Science Engineering, Gayatri Vidya Parishad College of Engineering for women, Visakhapatnam, India
3. Maddi Sahithi,Student, Department of Computer Science Engineering, Gayatri Vidya Parishad College of Engineering for women, Visakhapatnam, India
4.Boddu Hema Harshitha,Student, Department of Computer Science Engineering, Gayatri Vidya Parishad College of Engineering for women, Visakhapatnam, India
5. Bellamkonda Mohana Vyshnavi,Assistant Professor, Department of Computer Science Engineering, Gayatri Vidya Parishad College of Engineering for women, Visakhapatnam, India

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.

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Citation:

How to cite this article: M. Aswini, Gude Tejaswani, Maddi Sahithi, Boddu Hema Harshitha, and Bellamkonda Mohana Vyshnavi, Facial Emotion Based Stress Detection Using CNN and Haar Cascade Algorithms. International Journal of Microwave Engineering and Technology. 2024; 10(02): 41-50p.

How to cite this URL: M. Aswini, Gude Tejaswani, Maddi Sahithi, Boddu Hema Harshitha, and Bellamkonda Mohana Vyshnavi, Facial Emotion Based Stress Detection Using CNN and Haar Cascade Algorithms. International Journal of Microwave Engineering and Technology. 2024; 10(02): 41-50p. Available from:https://journalspub.com/publication/ijmet-v-10-i-02-2024-5/
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