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Articles by Mohammed M. Razooq
Total Records ( 1 ) for Mohammed M. Razooq
  Ghassan Jasim AL-Anizy , Md. Jan Nordin and Mohammed M. Razooq
  Driver drowsiness is the most critical cause of traffic accidents, thus drowsiness detection play a vital role in preventing traffic accidents. By developing an automatic solution for alerting drivers of drowsing, before an accident occurs, this could reduce the number of traffic accidents. Therefore, this research proposes a real-time detection approach for driver drowsiness. The proposed approach has two phases: image processing and machine learning. The role of image processing phase is to recognize the face of the driver and then extracts the image of the eyes of the driver. This phase uses Haar face detection algorithm that takes captured frames of image as input and then the detected face as output. Next, Haar is also used to extract the eyes image from the detected face which will be used as an input for the machine learning phase. The main role of the machine learning is to classify either the eyes of the driver are closed or opened using Support Vector Machine (SVM). If the result of the classification indicates that the driver’s eyes is closed for a predefined period of time, the eyes of the driver will be considered closed and hence an alarm will be started to alert the driver. The proposed methodology has been tested on available benchmark data. The result demonstrates the accuracy and robustness of the hybridized of image processing technique with machine learning technique. Thus, it can be concluded that the proposed approach is an effective solution method for a real-time of driver drowsiness detection.
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