Driver drowsiness detection by employing CNN and DLIB

Ali N., Hasan I., Özyer T., ALHAJJ R.

22nd International Arab Conference on Information Technology, ACIT 2021, Muscat, Oman, 21 - 23 December 2021 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/acit53391.2021.9677197
  • City: Muscat
  • Country: Oman
  • Keywords: CNN, Dlib, driver drowsiness, pytorch, Resnet
  • Istanbul Medipol University Affiliated: Yes


Every year thousands of people lose their life due to road accidents. One of the main reasons for these accidents is driver drowsiness. In driver drowsiness, the driver slept while driving, which causes the road accident, especially on the long routes. Driver fatigue and micro sleep while driving caused the fatal accident and death of human beings. To overcome this problem, we are implementing a technique in which we capture the image of the driver. After capturing the image of the driver, we process driver images to detect driver drowsiness. For the processing of the driver image, we are using two different techniques with each other. In the first technique, we are using the Dlib for image drowsiness detection by detecting that driver’s eyes are closed and the driver is yawning. In the second technique, we used CNN for the detection of yawning and the eyes of the driver are closed or not and predict driver drowsiness. After implementing the two techniques we combine the output of both techniques. After combining both techniques we test the system, and it gives us very good results.