Driver Drowsiness Detection System Using Visual Cues and Machine Learning

This research proposes a machine learning-based driver sleepiness monitoring system that analyzes visual behavior to ensure road safety. The method uses Convolutional Neural Networks (CNNs) and facial landmarks to identify signs of sleepiness, such as head tilt and eye closure rate. The motorist is alerted when drowsiness is detected by analyzing real-time images captured by a camera. This hardware-friendly and affordably priced solution, which simply requires a camera module, can be integrated into automobiles. With a focus on driver and passenger safety, the system promises a proactive approach to minimizing accidents caused by drowsiness.