Using Facial Features to Detect Driver’s Drowsiness State

Abstract

Abstract: Drowsiness and fatigue are one of the main causes leading to road accidents. They can be prevented by taking effort to get enough sleep before driving, drink coffee or energy drink, or have a rest when the signs of drowsiness occur. The popular drowsiness detection method uses complex methods, such as EEG and ECG [19]. This method has high accuracy for its measurement but it need to use contact measurement and it has many limitations on driver fatigue and drowsiness monitor. Thus, it is not comfortable to be used in real time driving. This paper proposes a way to detect the drowsiness signs among drivers by measuring the eye closing rate and yawning. We provide a robust and intelligent strategy for detecting driver tiredness in this work to address this growing problem. This method involves installing a camera inside the car to record the driver's facial look. The first phase involves using computer vision algorithms to identify and track the face region in the recorded video sequence. After that, the head is removed and its lateral and frontal assent are examined for indications of driver weariness. The driver's state is finally assessed during the fusion phase, and if drowsiness is found, a warning message and an alert are given to the driver. Our tests provide strong support for the proposed theory. The parameters of the eyes and mouth detection are created within the face image. The video was change into images frames per second. From there, locating the eyes and mouth can be performed. Once the eyes are located, measuring the intensity changes in the eye area determine the eyes are open or closed. The major goal of this research is to create a non-intrusive system that can recognise driver weariness and deliver a prompt warning.