Early Identification and Detection of Driver Drowsiness by Hybrid Machine Learning

Abstract

Drunkenness or tiredness is a leading cause of car accidents, and it has serious consequences for road safety. More fatal accidents can be avoided by alerting tired drivers ahead of time. While driving, several drowsiness detection technologies watch for signs of inattention and alert the driver. It is critical to be aware of such events to avoid potential danger by alerting the driver of their condition and automatically initiating emergency measures in real-time to ensure the safety of all passengers in the vehicle. To overcome this, we need a system that can continuously monitor the driver’s facial expressions and detect facial landmarks to extract their state of expression to determine whether they are sleepy or have extreme changes in their emotions, such as anger. As soon as the system detects such changes, it takes control of the vehicle, immediately slows it down, and alerts the driver by sounding an alarm to make them aware of the situation. The proposed system will be integrated with the vehicle’s electronics, tracking the vehicle’s statistics and providing more accurate results. In this paper, we have implemented real-time image segmentation and drowsiness using machine learning methodologies. In the proposed work, an emotion detection method based on Support Vector Machines (SVM) has been implemented using facial expressions. The algorithm was tested under variable luminance conditions and outperformed current research in terms of accuracy. We have achieved 83.25 % to detect the facial expression change.