Detecting Driver Drowsiness Using Eye State

Abstract

Various studies have suggested that 20%-50% road accidents are fatigue-related. Nevertheless, on certain roads, the maximum value is out of scope. This paper aims at developing a vision-based drowsiness detecting system which is noninvasive and accurate to help alert drowsy driver in advance. Eye state is used to evaluate drowsiness. First, face region is detected through AdaBoost with Haar feature. Then instead of applying old fashioned eye location methods which many drowsiness detect systems still possess, our system obtain eye region through a customized state of art face alignment algorithm based on Regressing Local Feature. Finally, an SVM is trained to distinguish eye states. Results demonstrate that our system can achieve a high detection rate in various lighting circumstances.