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Volume: 33 | Article ID: art00003
Real-time Detection of Early Drowsiness Using Convolution Neural Networks
  DOI :  10.2352/ISSN.2470-1173.2021.8.IMAWM-233  Published OnlineJanuary 2021

Drowsiness driving is one of the major reasons causing deadly traffic accidents in the United States of America. This paper intends to propose a system to detect different levels of drowsiness, which can help drivers to have enough time to handle sleepiness. Furthermore, we use distinct sound alarms to warn the user to prevent early accidents. The basis of the proposed approach is to consider symptoms of drowsiness, including the amount of eye closure, yawning, eye blinking, and head position to classify the level of drowsiness. We design a method to extract eye and mouth features from 68 key points of facial landmark. These features will help the system to detect the level of drowsiness in realtime video stream based on different symptoms. The experiential results show that the average accuracy of the system that has the capability to detect drowsiness intensity scale in different light conditions is approximately 96.6%.

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Chinh Tran, Nader Namazi, "Real-time Detection of Early Drowsiness Using Convolution Neural Networksin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2021,  pp 233-1 - 233-8,

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