Skip to main content

A CNN-Based Driver’s Drowsiness and Distraction Detection System

  • 100 Accesses

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13264)


The driver’s drowsiness and distraction are the principal causes of traffic accidents in the world. To attack this problem, in this paper we propose a visual-based driver’s drowsiness and distraction detection system, which is based on a face detection algorithm and a CNN-based driver state classification. To be useful the proposed system, we consider that the system must be implemented in a compact mobile device with limited memory space and computational power. The proposed system in compact mobile device can be used in any type of vehicle, avoiding accident caused by lack of driver’s alert. The proposed system is evaluated using public dataset, obtaining 95.77% of global accuracy. The proposed system is compared with five finetuned off-the-shelf CNNs, in which the proposed system shows a favorable performance, providing higher operation speed and lower memory requirement compared with these five CNNs, although the detection accuracy is slightly lower compared with the best CNN. The performance of the proposed system guarantees the real-time operation in the compact mobile device.


  • Convolutional Neural Networks (CNN)
  • Driver’s drowsiness detection
  • Driver’s distraction detection
  • Real-time implementation
  • Finetuning model

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-031-07750-0_8
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-031-07750-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   79.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.


  1. Facts and Stats. Accessed 30 Jan 2022

  2. Chacon-Murguia, M., Prieto-Resendiz, C.: Detecting driver drowsiness: a survey of system designs and technology. IEEE Consum. Electron. Mag. 4(4), 107–119 (2015)

    CrossRef  Google Scholar 

  3. Páez, M., Abarca, E.: Tools for security in movements, Predictive models of driver’s drowsiness. Accessed 30 Jan 2022

  4. Wang, J., Zhu, S., Gong, Y.: Driving safety monitoring using semisupervised learning on time series data. IEEE Trans. Intell. Transp. Syst. 11(3), 728–737 (2010)

    CrossRef  Google Scholar 

  5. Wu, B., Chen, Y., Yeh, C., Li, Y.: Reasoning-based framework for driving safety monitoring using driving event recognition. IEEE Trans. Intell. Transp. Syst. 14(3), 1231–1241 (2013)

    CrossRef  Google Scholar 

  6. Kokonogi, A., Michail, E., Chouvarda, I., Maglaveras, N.: A study of heart rate and brain system complexity and their interaction in sleep-deprived subjects. In: Proceeding on Computational Cardiology, pp. 969–971 (2008)

    Google Scholar 

  7. Zhang, C., Wang, H., Fu, R.: Automated detection of driver fatigue based on entropy and complexity measures. IEEE Trans. Intell. Transp. Syst. 15(1), 168–177 (2014)

    CrossRef  Google Scholar 

  8. Zhao, G., He, Y., Yang, H., Tao, Y.: Research on fatigue detection based on visual features. IET Image Process. 16, 1–20 (2020)

    Google Scholar 

  9. Phan, A., Nguyen, N., Trieu, T., Phan, T.: An efficient approach for detecting driver drowsiness based on deep learning. Appl. Sci. 11, 8441 (2021)

    CrossRef  Google Scholar 

  10. Flores-Monroy, J., Nakano-Miyatake, M., Perez-Meana, H., Sanchez-Perez, G.: Visual-based real time driver drowsiness detection system using CNN. In: Proceedings of International Conference on Electrical Engineering, Computing Science and Automatic Control, IEEE, Mexico City, Mexico (2021)

    Google Scholar 

  11. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR), San Diego (2015)

    Google Scholar 

  12. Flores, M., Armingo, J., De la Escalera, A., Elissa, K.: A. Real-time warning system for driver drowsiness detection using visual information. J. Intell. Rob. Syst. C. 69(2), 103–125 (2010)

    Google Scholar 

  13. Weng, C., Lai, Y., Lai, S.: Driver drowsiness detection via a hierarchical temporal deep belief network. In: Proceedings of the Asian Conference on Computer Vision, pp. 117–133. IEEE, Taipei, Taiwan (2016)

    Google Scholar 

  14. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520. IEEE, Salt Lake City (2018)

    Google Scholar 

  15. He,. K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE, Las Vegas (2016)

    Google Scholar 

  16. Viola, P., Jones, M.: Robust real-time object detection. In: Proceedings of the 2nd International Workshop on Statistical and Computation Theories of Vision – Modeling, Learning, Computing and Sampling, p. 25 (2001)

    Google Scholar 

  17. Bazarevsky, V., Kartynnik, Y., Vakunov, A., Raveendran, K., Grundmann, M.: BlazeFace: sub-millisecond neural face detection on mobile GPUs. In: Proceedings of Computer Vision & Pattern Recognition (2019)

    Google Scholar 

  18. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9, IEEE, Boston (2015)

    Google Scholar 

  19. Chollet, F.: Xception: deep learning with Depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1800–1807, IEEE, Honolulu (2017)

    Google Scholar 

  20. Chen, W., et al.: YOLO-face: a real-time face detector. Vis. Comput. 37(4), 805–13 (2021)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Mariko Nakano-Miyatake .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Flores-Monroy, J., Nakano-Miyatake, M., Perez-Meana, H., Escamilla-Hernandez, E., Sanchez-Perez, G. (2022). A CNN-Based Driver’s Drowsiness and Distraction Detection System. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07749-4

  • Online ISBN: 978-3-031-07750-0

  • eBook Packages: Computer ScienceComputer Science (R0)