Stress Detection System (AI + Computer Vision) Project Overview: I developed an AI-powered Real-Time Stress Detection System that uses a standard webcam to analyze a person’s emotional state and physiological signals. The system combines Facial Emotion Recognition (FER) and remote Photoplethysmography (rPPG) to generate an accurate stress score in real time. What the System Does: Captures live video through a webcam or mobile camera Detects and tracks the face using AI Identifies emotions such as anger, fear, sadness, happiness, neutral, disgust Extracts heart rate (BPM) from facial color variations using rPPG Calculates a combined stress level (Low / Medium / High) Displays live output on the screen Optionally triggers alerts or stores stress logs for analysis Technologies Used: Python OpenCV MediaPipe / DeepFace (for facial emotion detection) rPPG algorithms (CHROM / POS) for heart-rate estimation NumPy / SciPy Streamlit or Flask (optional front-end UI) Key Features: Real-time Facial Emotion Recognition Contactless heart-rate estimation Combined stress scoring algorithm High accuracy due to multi-parameter analysis Can be deployed as a web app, mobile app, or desktop application Suitable for corporate wellness, student monitoring, driver stress detection, healthcare applications, and academic research Why This Project Matters: This system helps identify stress early, enabling better mental wellness, productivity, safety, and decision-making. It serves as an affordable, contactless stress assessment solution that works using just a standard camera, without the need for specialized hardware. Research Contribution: This project has also been submitted as a conference paper, demonstrating its research potential and real-world applicability in AI-driven mental health monitoring.