Driver Drowsiness Detection System

Manjunath S, Banashree P, Shreya M, Sneha Manjunath Hegde, Nischal H P
2022 International Journal for Research in Applied Science and Engineering Technology  
Abstract: Recently, in addition to autonomous vehicle technology research and development, machine learning methods have been used to predict a driver's condition and emotions in order to provide information that will improve road safety. A driver's condition can be estimated not only by basic characteristics such as gender, age, and driving experience, but also by a driver's facial expressions, bio-signals, and driving behaviours. Recent developments in video processing using machine learning
more » ... ave enabled images obtained from cameras to be analysed with high accuracy. Therefore, based on the relationship between facial features and a driver's drowsy state, variables that reflect facial features have been established. In this paper, we proposed a method for extracting detailed features of the eyes, the mouth, and positions of the head using OpenCV and Dlib library in order to estimate a driver's level of drowsiness. Keywords: Drowsiness, OpenCV, Dlib, facial features, video processing
doi:10.22214/ijraset.2022.42109 fatcat:tczyrxfcsjfnbmnyvbwuqdoxui