Drowsiness Detection Using Deep Neural Network - et. al., K. Mirunalini, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
One of the major reasons for road accidents is driver’s drowsiness which causes several fatalities every year. Various studies on road accidents have proved that 20% of the accidents are caused mainly due to drowsiness among drivers while driving. In this paper, the system records the video and the NTHU Drowsiness Detection datasets for detecting the driver’s drowsiness using Image processing techniques. The investigator has also implemented DNN in the both video and datasets to extract the 63 features of a face. These were used to evaluate three measures like yaw, pitch, roll, and eye aspect ratio in order to find the distance between every feature of the face to detect the drowsiness of the driver. Experimental outcomes shows that our framework is better than the existing drowsiness detection methods based on visual analysis