Abstract
One of the main causes of traffic accidents is driver fatigue and drowsiness. Globally, they are increasing the number of fatalities and injuries each year. In this paper, main module focuses on how to recognize tiredness, which will assist to decrease accidents and improve roadsafety. This system gathers photos from a live webcam feed, applies machine learning to the image, and determines whether the driver is sleepy. Many facial expressions and bodily movements, including yawning and sleepy eyes, seen as indicators ofsleepiness. The EAR (Eye Aspect Ratio) measures the ratio of distances between the horizontal and vertical eye landmarks to identify sleepiness. The distance between the lower and upper lips used to produce a YAWN value for yawn detection, and the result is compared to a threshold value. We have used a play sound library, which will giveappropriate voice alert messages when the driver is in a drowsy state. A time limit is applied for driving, if the driver exceeds the time limit then the also system gives the alert message. The proposed system madeto decrease the rate of accidents.
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