Facial Emotion-Based Stress Analysis Using Python

In the current fast-paced world, tracking mental health has become imperative. This project demonstrates a real-time stress detection and analysis system based on facial emotion recognition through deep learning approaches. It automatically detects emotional states from facial images to measure the stress level of an individual. A Convolutional Neural Network (CNN) is trained on facial image dataset with primary emotions like angry, sad, happy, and neutral, which are typically associated with different stress conditions. The system is implemented in Python and TensorFlow, supported by libraries such as OpenCV for image processing and Keras for constructing the deep learning model. It can take input from live webcam feed or preloaded static images, performing preprocessing and real-time facial emotion classification. The results are displayed in the form of graphs and charts, providing an overview of the user’s stress trends over a period. Development and testing were conducted in a simulated environment using software such as Visual Studio Code and Jupyter Notebook. The results demonstrate the efficiency of the system in detecting and measuring stress levels, useful for early mental health detection, workplace wellness tracking, and improving human-computer interaction. Future work can be in the direction of larger datasets and incorporating physiological signals for multi-modal stress detection..