Enhancing Real-Time Driver Drowsiness Detection System with Facial Recognition Using Support Vector Machine

The aim of the research is to detect driving fatigue and determine whether driver sleeping or awake. There are several approaches for detecting tiredness based on extensive learning, but in this instance we will use machine learning algorithms. alarm sounds when becomes sleepy prevent accidents. Factors compared using various groups, 20 samples used obtain an 80 percent Gpower accuracy rate. EAR method (84.60 percent) SVM algorithm (90.00 both have high retrieval accuracy. data gathered from resources networking capabilities that were previously context-sensitive, such as applying brakes via system, alerted OpenCV package Python environment by employing two techniques.