FatiguEye
Detection of fatigue using a webcam
A smart computer vision system that detects signs of fatigue, eye strain, and microsleep using a standard webcam.
🎯 Purpose
FatiguEye is a real-time fatigue detection system based on eye tracking and facial landmark analysis.
It helps identify early signs of drowsiness by measuring:
- 👁️ Eye Aspect Ratio (EAR)
- 🔁 Blink frequency
- ⏱️ Prolonged eyelid closure
- ⚠️ Microsleep events
Ideal for driver monitoring, industrial safety, or ergonomic fatigue prevention.
🧠 How It Works
FatiguEye uses MediaPipe Face Mesh to extract eye landmarks, and computes the EAR (Eye Aspect Ratio) on each video frame.
📡 Processing pipeline:
- 🎥 Webcam feed is captured in real-time
- 🧠 Facial landmarks (eyes) are detected with Mediapipe
- 📏 EAR is calculated per eye
- 🧮 Blink count and eye closure duration are analyzed
- 🔔 Fatigue alerts are raised (visual + audio)
🚀 Demo Preview
💻 Technologies Used
| Tech | Description |
|---|---|
| Python | Core language |
| OpenCV | Webcam video processing + overlays |
| MediaPipe | Face mesh & eye landmark detection |
| NumPy | EAR computation |
| Streamlit | Live web dashboard |
| winsound | Audio alert (Windows only) |
📦 Installation
```bash git clone https://github.com/Tirovo/fatigueye.git cd fatigueye python -m venv venv source venv/bin/activate # Or venv\Scriptsctivate on Windows