Abstract
Abstract: Various studies indicate that fatigueness in drivers leads to road accidents. It can lead to serious injuries like brain damage or it can lead to death. Therefore preventing people from harming the countermeasure device is necessary as a reliable solution. This study therefore came up with a new way of explaining the driver's fatigueness. This example uses the Haar Cascade algorithm, next to the OpenCV library to keep an eye on the real-time video of the driver and critique the driver's eyes. The Eye Aspect Ratio (EAR) is used in this measurement device to determine if the eyes are open or closed. The Mouth Aspect Ratio (MAR) is also used as an important element while this model describes the driver's fatigueness as the driver begins to yawn just before the driver feels fatigue. If the driver is found to be fatigue, a warning signal is issued. Keywords: OpenCV Automated algorithms, database, Image recognition, face detection, frontal postures, extraction phase, OpenCV, data models, HaarCascade classifier, training set.
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