Mobile-based driver sleepiness detection using facial landmarks and analysis of EAR Values

Abstract

Sleepiness during driving is a dangerous problem faced by all countries. Many studies have been conducted and stated that sleepiness threatens the driver himself and other peoples. The victim not only suffered minor injuries, but also many of them ended in death. Nowadays, there are many kinds of studies to improve sleep detection methods. But it faces difficulties such as lack of accuracy and poor performance of detection; thus, the system inadequate works in real-time. Recently, automobile companies have begun manufacturing special equipment to recognize sleepiness driver. However, the technologies are only implemented in certain cars since the price is still quite expensive. Therefore, a system with a comprehensive method is needed to discover the driver's sleepiness accurately at an affordable price. This study proposed driver sleepiness detection implemented on a smartphone. The system is capable of identifying closed eyes using the extraction of Facial Landmark points and analysis of calculation results of the Eye Aspect Ratio (EAR). The system qualified works in real-time since it uses a particular library designed in a mobile application. Based on some experiments that have been done, the proposed method adequate to identify sleepy driver accurately by 92.85%.