Drowsiness Detection Using Haar and CNN Algorithm

Abstract

The objective of this project is to create a drowsiness detection system that can recognize when someone's eyes are closed for a brief period of time. When sleepiness is detected, this system will give the user a warning. When someone is falling asleep, an alarm buzzes to wake them up. Making the model platform independent, computationally efficient, and affordable for the low-end spec platform is the main goal of this project. Furthermore, to boost the detection's face-sensing accuracy, a mixture of two improved algorithms is applied. The existing system occasionally generates false positive results, which results in erroneous drowsiness detections. These systems might not function properly in various lighting scenarios or with different facial expressions. The proposed system is made with the intention of reducing accident rates and advancing technology in order to reduce the number of deaths and injuries brought on by traffic accidents.