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Drowsiness is defined as the "inclination to sleep" and is also commonly referred to as "sleepiness". It is a natural occurrence in the human body that can affect individuals in different ways. Driver impairment due to drowsiness is known to be a major contributing factor in many motor vehicle crashes. Hence, the need of a reliable driver drowsiness detection system which could alert drivers before a mishap happens. In this paper, a drowsy driver detection system has been developed, using video processing analyzing eyes blinking concepts for measuring eyes closure duration to verify the driver vigilance state. In such a case, when this duration exceeds a specific time, a warning signal is issued to alert the driver. To achieve this application, three main steps are required: face and eyes detection, classification and eyes tracking and measurement of eyes closure duration. To accomplish this work, eyes are tracked and recognized employing wavelet network classifier (WNC) based on fast wavelet transform (FWT) for its robustness and for its pertinent results in the classification's domain. Experiments show that our used methods are effective for driver drowsiness detection.

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