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.
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References
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
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
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
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
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
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
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
Orazioa TD, Leo M, Guaragnellab C, Distante A (2007) A visual approach for driver inattention detection. Pattern Recognit 40:2341–2355
Ji Q, Yang X (2002) Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real Time Imaging 8:357–377
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
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
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
Saradadevi M, Bajaj P (2008) Driver fatigue detection using mouth and yawning analysis. Int J Comput Sci Netw Secur 8(6):183–188
Hu Q, Chen D, Yu D, Pedrycz W (2009) Kernelized fuzzy rough sets. Lect Notes Comput Sci 5589:304–311
Du Y, Hu Q, Zhu P, Ma P (2011) Rule learning for classification based on neighborhood covering reduction. Inf Sci 181(24):5457–5467
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
Zhou L, Wu WZ (2008) On generalized intuitionistic fuzzy rough approximation operators. Inf Sci 178(11):2448–2465
Zhao S, Tsang ECC, Chen D (2009) The model of fuzzy variable precision rough sets. IEEE Trans Fuzzy Syst 17(2):451–467
Ciucci D (2006) On the axioms of residuated structures: independence, dependencies and rough approximations. Fundam Inform 69(4):359–387
Maji P, Pal SK (2007) RFCM: a hybrid clustering algorithm using rough and fuzzy sets. Fundam Inform 80(4):475–496
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
Roweis T, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326
Saul LK, Roweis ST (2003) Think globally, fit locally: unsupervised learning of low dimensional manifolds. Mach Learn Res 4:119–155
Moser B (2006) On the T-transitivity of kernels. Fuzzy Sets Syst 157(13):1787–1796
Moser B (2006) On representing and generating kernels by fuzzy equivalence relations. Mach Learn Res 7:2603–2620
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers, Burlington
Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106
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
Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 38(2):337–374
Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297–336
Vezhnevets A, Vezhnevets V (2005) Modest AdaBoost—teaching AdaBoost to generalize better. Graphicon 12(5):987–997
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556
Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. In: British machine vision conference (BMVC)
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)
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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
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DOI: https://doi.org/10.1007/s11063-017-9698-z