Driver drowsiness is a major contributor to road accidents. This Driver Drowsiness Detection system uses Siamese Neural Networks and computer vision techniques to monitor a driver's facial features in real-time and detect signs of fatigue. By analyzing eye states, head pose, and mouth movements (including yawning), the system identifies potential drowsiness and enhances road safety.
- Real-Time Drowsiness Detection: Monitors the driver's face continuously for signs of fatigue.
- Eye Aspect Ratio (EAR): Utilizes EAR to determine prolonged eye closures, a common sign of drowsiness.
- Head Pose Estimation: Detects abnormal head movements such as nodding or tilting.
- Mouth and Yawning Detection: Analyzes mouth movements using computer vision techniques to identify yawning, another common fatigue indicator.
- Siamese Neural Networks: All detection (eye, head pose, and yawning) is powered by a Siamese Neural Network, which allows the system to effectively compare facial features and detect signs of drowsiness.
- Python 3.8+
- Tensorflow & Keras (Siamese Neural Networks) for feature comparison and detection.
- OpenCV for real-time computer vision tasks.
- Dlib for facial landmark detection.
- Imutils for image processing utilities.