ML-Driven Real Time Emotion and Stress Level Monitoring

This project uses a facial expression recognition (FER) model to detect emotions and estimate stress levels in real time. It works by tracking faces, extracting facial features, and classifying emotions using machine learning. The system then analyzes these emotions to assess potential stress levels.

The model is built in Python and works on both Windows and Linux, though it has mainly been tested on Windows. It uses OpenCV for real-time face detection and integrates deep learning models like CNN for accurate emotion classification. The system is designed to process data quickly, making it useful for applications like mental health monitoring, human-computer interaction, and security surveillance.

Note: This project is being developed and tested exclusively on Google Colab.