Paper Contents
Abstract
- Stress is a common psychological condition that can adversely affect both mental and physical health. This project proposes a real-time stress detection system using facial expressions as the primary input. By leveraging computer vision and deep learning techniques, the system analyzes facial features captured through a webcam or uploaded image and classifies the user's emotional state to determine stress levels. The backend integrates DeepFace for facial analysis and a Flask server to process and display results through a user-friendly web interface. The system aims to provide individuals with immediate insights into their stress condition, enabling early intervention and promoting mental well-being. Additionally, the system suggests evidence-based stress reduction techniques to help users manage and lower their stress levels effectively.The proposed system aims to detect stress levels using facial expressions through real-time video or image input. Leveraging DeepFace for facial emotion recognition and a Flask-based backend, the system identifies emotional indicators correlated with stress. A user-friendly web interface allows individuals to receive instant feedback on their stress condition. The primary goal is to aid in early detection and encourage proactive stress management. The system also offers practical stress reduction tips, promoting mental well-being and resilience.Keywords: Python,Image processing, KNN classifier, Open CV, Machine learning,Haarcasade Algorithm
Copyright
Copyright © 2025 V. Lakshmi sashi. This is an open access article distributed under the Creative Commons Attribution License.
