Development of an Automated Tool for Driver Drowsiness Detection

It is important to identify ocular sleepiness as soon as possible to prevent accidents. More than 1.3 million people lose their lives in traffic-related incidents every year. In such incidents, the majority (78.4%) of collisions are the result of driver error, including excessive speeding, intoxication, and sleepy driving, which accounts for 21% of collisions. In order to prevent accidents, we should warn the motorist before they happen. This study uses face detection to identify the driver’s face and then extract characteristics to identify their level of tiredness. The system then warns the motorist before a potential collision.