Acta Scientific Computer Sciences

Research Article Volume 4 Issue 3

Driver Drowsiness Detection

Hemang Thakur1, Deval Arora1, Ramkiran Sampathi1, Shree Rukmini Thumu1 and Parisa Naraei2*

1Department of Artificial Intelligence and Machine Learning Lambton College Toronto, Canada
2Cestar College of Business, Health and Technology Research and Innovation Center, Lambton College Toronto, Canada

*Corresponding Author: Parisa Naraei, Cestar College of Business, Health and Technology Research and Innovation Center, Lambton College Toronto, Canada.

Received: November 27, 2021; Published: February 24, 2022


CNN models are widely used to implement business solutions in the computer vision domain. In this paper, we have built a CNN based face detection model along with a facial feature detection using dlib library as accurate and light weight available libraries. This allows these models to run efficiently even on low-power devices. As a proof of concept, the paper presents a desktop application of the effective solution in the real-world scenario. In this approach, the images are captured at an interval of 0.1 seconds as the models take roughly 50 to 80 milliseconds to predict the output for a single image. As a result, the model is able to detect the driver drowsiness with an accuracy of 92.5% taking into account the state of mouth as well as eyes.

Keywords: Face Detection; Drowsy Driving; Convolution Neural Networks; Real Time Image Analysis


  1. Wilbert O Galitz. “The Essential Guide To User Interface Design” (2007).
  2. David Amos. “Python GUI Programming with Tkinter” (2012).
  3. Primoz Podrzaj. “A brief demonstration of some Python GUI libraries” (2019).
  4. Guilherme Polo. The Python Papers 2.1.
  5. I Culjak., et al. "A brief introduction to OpenCV". 2012 Proceedings of the 35th International Convention MIPRO, Opatija, Croatia (2012): 1725-1730.
  6. Grant Zhong. “Drowsiness Detection with Machine Learning” (2019).
  7. M Ngxande., et al. "Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques". 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), Bloemfontein, South Africa (2017): 156-161.
  8. Phakawat Pattarapongsin. “Real-time Drowsiness and Distraction Detection using Computer Vision and Deep Learning” (2020).
  9. H Garg. "Drowsiness Detection of a Driver using Conventional Computer Vision Application". 2020 International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC) (2020): 50-53.
  10. Gauri Thakre. “Drowsy Driver Detection and Alert System” (2018).
  11. C Sagonas., et al. “300 faces In-the-wild challenge: Database and results”. Image and Vision Computing (IMAVIS) (2016).
  12. C Sagonas., et al. “300 Faces in-the-Wild Challenge: The first facial landmark localization”. Challenge (2013).
  13. Tashakori M., et al. “Driver drowsiness detection using facial thermal imaging in a driving simulator”. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine1 (2022): 43-55.
  14. Babu A., et al. “Driver’s Drowsiness Detection System Using Dlib HOG”. In: Karuppusamy P., Perikos I., García Márquez F.P. (eds) Ubiquitous Intelligent Systems. Smart Innovation, Systems and Technologies, 243 (2022).
  15. Ajinkya Rajkar., et al. “Driver Drowsiness Detection Using Deep Learning” (2021).
  16. Mohit Dua., et al. “Deep CNN models-based ensemble approach to driver drowsiness detection”. Neural Computing and Applications 33 (2021): 3155-3168.
  17. Mahmoodi M and Nahvi A. “Driver drowsiness detection based on classification of surface electromyography features in a driving simulator”. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine4 (2019): 395-406.
  18. Houshmand S., et al. “A novel convolutional neural network method for subject-independent driver drowsiness detection based on single-channel data and EEG alpha spindles”. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine9 (2021): 1069-1078.
  19. Emashharawi MJS., et al. “Computer Vision Based Driver Assistance Drowsiness Detection”. In: Isa K. et al. (eds) Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020. Lecture Notes in Electrical Engineering 770 (2022).
  20. Bhatia U., et al. “Drowsiness Image Detection Using Computer Vision”. In: Sharma T.K., Ahn C.W., Verma O.P., Panigrahi B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing 1380 (2022).
  21. Rahman NAA., et al. “EMG Signal Segmentation to Predict Driver’s Vigilance State”. In: Hassan M.H.A. et al. (eds) Human-Centered Technology for a Better Tomorrow. “Lecture Notes in Mechanical Engineering”. Springer, Singapore (2022).


Citation: Parisa Naraei., et al. “Driver Drowsiness Detection". Acta Scientific Computer Sciences 4.3 (2022): 08-13.


Copyright: © 2022 Parisa Naraei., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
Acceptance to publication20-30 days

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