Real Time Driver Fatigue Detection Based on Facial Behaviour along with Machine Learning Approaches

This paper is concerned about the detection procedure of drowsiness that causes fatal road accidents leading to death. Increasing lack of awareness of traffic rules is doubling the number of accidents daily. However, detection and indication of driver fatigue is an active area of research. In this concussion, both inside and outside individuals of the car are victimized. However, prevention of drowsiness requires a technique to detect the deterioration legitimately along with a warning mechanism of the vehicle operator. Although the existing solutions are created using some distinctive methods, there are some problems such as costly sensors and handling of information. The objective of this research is to create an improved, innovative, cost efficient and real time strategy for solving this problem of blinking, yawn, and head bending. A pre-trained model by a histogram-oriented gradient (HOG) and a linear vector support machine (SVM) extracts the eye, nose and mouth position and assesses the eye aspect ratio (EAR), moutt opening ratio (MOR) and nose length ratio (NLR). These pieces of information are then compared with the value threshold adapted from the sleeping or drowsy face models aspect ratio data set.