Real-Time Stress Detection System with Integrated Facial Expression Analysis based Hybrid Neural Network

In order to address the growing concerns about stress, this project develops a state-of-the-art, multi-modal stress detection system that integrates emotion analysis with facial recognition. The system analyses facial pictures in real time by using a pre-trained Convolutional Neural Network (CNN) for stress estimate and OpenCV's Haar cascade for precise face identification. A k-Nearest Neighbours (KNN) algorithm is used to better examine emotional signals, improving stress assessment by combining emotional and physiological signs. Offering a cutting-edge method of mental health monitoring and intervention, this potent, real-time solution is perfect for a variety of applications, including personal well-being and business settings. The fusion model achieves the accuracy of 96% which is higher than the traditional methods for predicting the stress detection.