Skip to main content
Log in

Driver State Analysis Based on Imperfect Multi-view Evidence Support

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Driver state analysis is considered as a potential application of computer vision. Facial images contain important information that enable recognition of the states of a driver. Unfortunately, the information hidden in facial images is imperfect and varies with the external environments. Modeling the relationship between the face information and driver’s state plays an essential role in driver fatigue detection. In this work, facial sequences are aligned and normalized, following which, a few fixed observation areas related to the fatigue expressions are extracted. Some discriminative features are extracted to represent facial states from these areas. A single image does not contain enough information to reflect fatigue expressions, hence a sequence of face images are exploited for fatigue detection using a sliding window. Thus, both static and sequential information are used to represent the states of a driver. An algorithm is designed to evaluate the quality of the extracted candidate features. Each area only contains partial information for state recognition, and merely provides a single view of the evidence for driver state recognition. We built base models with the information extracted from some specific facial areas, and integrated these to recognize the states of the driver. Experimental results show that these base models can offer complementary information for accurately identifying the facial status, and the integrated model shows good performance in driver state analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Jung SJ, Shin HS, Chung WY (2014) Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel. IET Intell Transp Syst 8(1):43–50

    Article  Google Scholar 

  2. Ji Q, Zhu Z, Lan P (2004) Real time non-intrusive monitoring and prediction of driver fatigue. IEEE Trans Veh Technol 53(4):1052–1068

    Article  Google Scholar 

  3. Teyeb I, Jemai O, Zaied M, Ben Amar C (2014) A novel approach for drowsy driver detection using head posture estimation and eyes recognition system based on wavelet network. In: Proceedings of 5th international IEEE conference on information, intelligence, systems and applications, Chania, Greece, pp 379–384

  4. Du Y, Hu Q, Chen D, Ma P (2011) Kernelized fuzzy rough sets based yawn detection for driver fatigue monitoring. Fundam Inform 111(1):65–79

    MathSciNet  Google Scholar 

  5. Chiang CC, Tai WK, Yang MT, Huang YT, Jaung C (2003) A novel method for detecting lips, eyes and faces in real time. Real Time Imaging 9(4):277–287

    Article  Google Scholar 

  6. Chai R, Naik G, Nguyen TN et al (2017) Driver fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system. IEEE J Biomed Health Inform 21(3):715–724

  7. Lin CT, Chuang CH, Huang CS et al (2014) Wireless and wearable EEG system for evaluating driver vigilance. IEEE Trans Biomed Circuits Syst 8(2):165–176

    Article  Google Scholar 

  8. Orazioa TD, Leo M, Guaragnellab C, Distante A (2007) A visual approach for driver inattention detection. Pattern Recognit 40:2341–2355

    Article  MATH  Google Scholar 

  9. Ji Q, Yang X (2002) Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real Time Imaging 8:357–377

    Article  MATH  Google Scholar 

  10. Xie J, Xie M, Zhu W (2012) Driver fatigue detection based on head gesture and PERCLOS. In: International conference on wavelet active media technology and information processing, Chengdu, China, pp 128–131

  11. Qing W, BingXi S, Bin X et al (2010) A perclos-based driver fatigue recognition application for smart vehicle space. In: Proceedings of the 3rd international IEEE symposium on information processing, pp 437–441

  12. Wang RB, Guo L, Tong BL, Jin LS (2004) Monitoring mouth movement for driver fatigue or distraction with one camera. In: Proceedings of the 7th international IEEE conference on intelligent transportation systems (ITS), pp 314–319

  13. Saradadevi M, Bajaj P (2008) Driver fatigue detection using mouth and yawning analysis. Int J Comput Sci Netw Secur 8(6):183–188

    Google Scholar 

  14. Hu Q, Chen D, Yu D, Pedrycz W (2009) Kernelized fuzzy rough sets. Lect Notes Comput Sci 5589:304–311

    Article  Google Scholar 

  15. Du Y, Hu Q, Zhu P, Ma P (2011) Rule learning for classification based on neighborhood covering reduction. Inf Sci 181(24):5457–5467

    Article  MathSciNet  Google Scholar 

  16. Zhu P, Hu Q, Han Y, Zhang C, Du Y (2016) Combining neighborhood separable subspaces for classification via sparsity regularized optimization. Inf Sci 370–371:270–287

    Article  Google Scholar 

  17. Zhou L, Wu WZ (2008) On generalized intuitionistic fuzzy rough approximation operators. Inf Sci 178(11):2448–2465

    MathSciNet  MATH  Google Scholar 

  18. Zhao S, Tsang ECC, Chen D (2009) The model of fuzzy variable precision rough sets. IEEE Trans Fuzzy Syst 17(2):451–467

    Article  Google Scholar 

  19. Ciucci D (2006) On the axioms of residuated structures: independence, dependencies and rough approximations. Fundam Inform 69(4):359–387

    MathSciNet  MATH  Google Scholar 

  20. Maji P, Pal SK (2007) RFCM: a hybrid clustering algorithm using rough and fuzzy sets. Fundam Inform 80(4):475–496

    MathSciNet  MATH  Google Scholar 

  21. Mazumder RU, Begum SA, Biswas D (2013) An exponential kernel based fuzzy rough sets model for feature selection. Int J Comput Appl 81(6):24–31

    Google Scholar 

  22. Roweis T, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326

    Article  Google Scholar 

  23. Saul LK, Roweis ST (2003) Think globally, fit locally: unsupervised learning of low dimensional manifolds. Mach Learn Res 4:119–155

    MathSciNet  MATH  Google Scholar 

  24. Moser B (2006) On the T-transitivity of kernels. Fuzzy Sets Syst 157(13):1787–1796

    Article  MathSciNet  MATH  Google Scholar 

  25. Moser B (2006) On representing and generating kernels by fuzzy equivalence relations. Mach Learn Res 7:2603–2620

    MathSciNet  MATH  Google Scholar 

  26. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers, Burlington

    Google Scholar 

  27. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Google Scholar 

  28. Fan X, Sun YF, Yin BC, Guo XM (2010) Gabor-based dynamic representation for human fatigue monitoring in facial image sequences. Pattern Recognit Lett 31(3):234–243

    Article  Google Scholar 

  29. Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 38(2):337–374

    Article  MathSciNet  MATH  Google Scholar 

  30. Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297–336

    Article  MATH  Google Scholar 

  31. Vezhnevets A, Vezhnevets V (2005) Modest AdaBoost—teaching AdaBoost to generalize better. Graphicon 12(5):987–997

    Google Scholar 

  32. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556

  33. Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. In: British machine vision conference (BMVC)

Download references

Acknowledgements

This study was funded by the National Natural Science Foundation of China (NSFC) (51308096), Foundation of Education Department of Heilongjiang Province (12541050), China Postdoctoral Science Foundation (2014M551024), and Major Scientific Research Program of Beijing Wuzi University (0541603901)

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qinghua Hu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, Y., Wang, Y., Huang, X. et al. Driver State Analysis Based on Imperfect Multi-view Evidence Support. Neural Process Lett 48, 195–217 (2018). https://doi.org/10.1007/s11063-017-9698-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-017-9698-z

Keywords

Navigation