DRIVER FATIGUE DETECTION METHOD BASED ON FACIAL FEATURES BY USING DEEP LEARNING

Abstract

Fatigue detection using face recognition, powered by Dlib and CNN, addresses a critical road safety issue by identifying driver fatigue, a major cause of accidents. This system analyzes facial features like eye closure, yawning, and head position to detect early signs of fatigue in real-time. Leveraging Dlib's fast and accurate facial landmark detection, the model continuously monitors the driver and issues alerts when fatigue is detected, enabling timely intervention. Trained on a diverse dataset of facial images, the system ensures high accuracy across various lighting and environmental conditions, offering a cost-effective solution to reduce fatigue-related accidents and enhance road safety.