Stress Sense: Enhanced Stress Detection and Management Via Image Processing

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.