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Eye Based Drowsiness Detection System for Driver
Purnamasari Prima Dewi,Kriswoyo Arie,Ratna Anak Agung Putri,Sudiana Dodi 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.1
Most traffi c accidents are caused by human error, i.e. drowsiness. A drowsiness detection system is then developed to respond to this situation. In this work, the drowsiness detection system is built through the OpenCV library by combining the Haar Cascade Classifi er algorithm with Blur, Canny, and Contour function. Haar Cascade Classifi er was used to detect areas of face and eyes whereas the combination of Blur, Canny, and Contour functions are used to detect the driver's eyes and analyze the opening or closing of the driver's eyes. The performance of the drowsiness detection system was tested through four variables; kernel size, threshold value, lighting condition (morning, noon, afternoon, and night), and eye's characteristic (eyeglasses or not). Based on the experiments, the best kernel size to detect the driver's eyes is 4,4. Then, the best lower threshold and upper thresholds are 70–110 and 210–240. Subsequently, the light conditions have a 20% error rate to the system. The eye's characteristic has a 16,7% error rate to the system.