Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver using Machine Learning Approach

The process of determining if a person, generally driver, is becoming sleepy or drowsy while performing task such as driving known drowsiness detection. It necessary system for detecting and alerting drivers to their tiredness, which might impair ability lead accidents. project aims create reliable efficient capable real-time detection using OpenCV, Dlib, facial landmark technologies. project's results show that the sleepiness method can accurately precisely identify tiredness in real time. technology less intrusive more economical than conventional techniques. based on 68 detector, highly trained effective detector recognizing human face points. aids assessing whether driver's eyes are closed open. analyses data collected by machine learning methods discover patterns associated with drowsiness. When detected, incorporates warning mechanism, an alarm vibration steering wheel, notify driver. A variety studies different conditions were used evaluate performance driver system. detect properly deliver timely warnings This assist preventing incidents, enhancing road safety, saving lives. indicated algorithm had average accuracy rate 94% identifying drivers.