Advertisement

A New Method for Driver Fatigue Detection Based on Eye State

  • Xinzheng XuEmail author
  • Xiaoming Cui
  • Guanying Wang
  • Tongfeng Sun
  • Hongguo Feng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9436)

Abstract

Fatigue driving is one of main problems threatening driving safety. Therefore, it attracts numerous researchers interests. This paper introduces a new method based on eye feature to research the fatigue driving. Firstly, the face is detected by the model of skin-color in the YCbCr color space, which extracted face region from complex background quickly and accurately. Secondly, eye detection includes extracting eye region and detecting eye two steps. Specifically, the proposed method extracts eye region in face image based on gray-scale projection and then detect eye using Hough transform. Finally, calculate the area of the eye profile after dilation and use it as the parameter to analysis eye state. Put forward the standard to recognize fatigue base on the PERCLOS. The experiment results illustrate the efficiency and accurately of the proposed method, especially, detected face as well as extracted eye region with a high accuracy.

Keywords

Fatigue detection Face detection Extracting eye region Eye detection 

Notes

Acknowledgments

This work is supported by the Basic Research Program (Natural Science Foundation) of Jiangsu Province of China (No.BK20130209), the Fundamental Research Funds for the Central Universities (No.2013QNA24), the Project Funded by China Postdoctoral Science Foundation (No.2014M560460), the Project Funded by Jiangsu Postdoctoral Science Foundation (No.1302037C).

References

  1. 1.
    Pei, Z., Song, Z.H., Zhou, Y.M.: Research status and development trend of motor vehicle driver fatigue evaluation method. J. China Agric. Univ. 6(6), 101–105 (2001)Google Scholar
  2. 2.
    Lampetch, S., Punsawad, Y., Wongsawat, Y.: EEG-based mental fatigue prediction for driving application. In: Biomedical Engineering International Conference (BMEICON), pp. 1–5 (2012)Google Scholar
  3. 3.
    Vicente, J., Laguna, P., Bartra, A., Bailon, R.: Detection of driver’s drowsiness by means of HRV analysis. In: Computing in Cardiology, pp. 89–92 (2011)Google Scholar
  4. 4.
    Wang, P., Shen, L.: A method detecting driver drowsiness state based on multi-features of face. In: 2012 5th International Congress on Image and Signal Processing (CISP 2012), pp. 1171–1175 (2012)Google Scholar
  5. 5.
    Lee, B.G., Chung, W.Y.: Driver alertness monitoring using fusion of facial features and bio-signals. IEEE Sens. J. 12(7), 2416–2422 (2012)CrossRefGoogle Scholar
  6. 6.
    Watta, P., Gandhi, N., Lakshmanan, S.: An Eigenface approach for estimating driver pose. In: 2000 Proceedings Intelligent Transportation Systems, pp. 376–381. IEEE (2000)Google Scholar
  7. 7.
    Ni, Q.K., Guo, C., Yang, J.: Research of face image recognition based on probabilistic neural networks. In: 2012 24th Chinese Control and Decision Conference (CCDC), pp. 3885–3888 (2012)Google Scholar
  8. 8.
    Shan, D., Ward, R.K.: Improved face representation by nonuniform multilevel selection of gabor convolution features. IEEE Trans. Sys. Man Cybern. Part B Cybern. 39(6), 1408–1419 (2009)CrossRefGoogle Scholar
  9. 9.
    Zhao, Y.L., Gao, Z., Wu, W.X.: The detection algorithm of locomotive driverss fatigue based on vision. In: 2010 3rd International Congress on Image and Signal Processing (CISP2010), pp. 2686–2690 (2010)Google Scholar
  10. 10.
    Devi, M.S., Choudhari, M.V., et al.: Driver drowsiness detection using skin color algorithm and circular hough transform. In: 2011 Fourth International Conference on Emerging Trends in Engineering and Technology, pp. 129–134 (2009)Google Scholar
  11. 11.
    Wu, C.D., Zhang, C.B.: Detecting and locating method of human face in driver fatigue surveillance. J. Shenyang Jianzhu Univ. Nat. Sci. 25(2), 386–389 (2009)Google Scholar
  12. 12.
    Lu, L., Yang, Y., Wang, L., Tang, B.: Eye location based on gray projection. In: 2009 Third International Symposium on Intelligent Information Technology Application, pp. 58–60 (2009)Google Scholar
  13. 13.
    Feng, J.Q., Liu, W.B., Yu, S.L.: Eyes location based on gray-level integration projection. Comput. Simul. 22(4), 75–76 (2005)Google Scholar
  14. 14.
    Yang, Q.F., Gui, W.H., et al.: Eye location novel algorithm for fatigue driver. Comput. Eng. Appl. 44(6), 20–24 (2008)Google Scholar
  15. 15.
    Qu, P.S., Dong, W.H.: Eye states recognition based on eyelid curvature and fuzzy logic. Comput. Eng. Sci. 29(8), 50–53 (2007)Google Scholar
  16. 16.
    Pan, X.D., Li, J.X.: Eye state-based fatigue drive monitoring approach. J. Tongji Univ. Nat. Sci. 39(2), 231–235 (2011)Google Scholar
  17. 17.
    Wang, Y., Hu, J.W.: A method for detection of driver eye fatigue state based on 3G video. Electron. Sci. Tech. 24(10), 84–85 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Xinzheng Xu
    • 1
    Email author
  • Xiaoming Cui
    • 1
  • Guanying Wang
    • 1
  • Tongfeng Sun
    • 1
  • Hongguo Feng
    • 2
  1. 1.School of Computer Science and TechnologyChina University of Mining and Technology XuzhouJiangsuChina
  2. 2.77626 Troops, Tibet Autonomous RegionTibetChina

Personalised recommendations