基于视频图像的人脸疲劳状态检测技术研究
摘要
疲劳驾驶可能导致军机军车事故,影响军事后勤运输保障;疲劳也可能导致边防值勤疏忽大意而造成严重后果。能够及时检测出疲劳状态并发出警示信号的疲劳检测技术可以有效预防失误以及事故发生,在人脸表情自动识别方面具有广泛的应用前景。基于机器视觉的方法与其他检测方法相比,具有实时性、准确性、非接触性、适用性及经济性等方面的优势,成为当前研究的一个热点。
针对全天候、全时段人脸检测的要求,本文提出一种以红外光照环境下的人脸视频图像为输入,基于AdaBoost的方法人脸区域检测算法。该方法首先利用人眼在红外光照条件下的红眼效应定位瞳孔;然后根据瞳孔定位剔除大部分非人脸区域;最后在候选区域内使用AdaBoost方法完成对人脸区域的检测定位。该方法简化了传统AdaBoost人脸检测的运算,大幅提高了人脸检测速度。
针对以往疲劳检测算法普遍存在的受光照条件影响大、检测速度慢以及可靠性差的问题,本文提出一种基于AdaBoost的疲劳表情快速检测算法。该算法首先对输入的红外视频图像进行人脸检测定位,然后将眼睛、嘴巴分割出来,分别提取两个子图块的PCA特征,分别输入事先经过AdaBoost训练得到两个分类器进行判别,将两个分类器的输出进行或运算得到最终的检测结果。实验表明,本文算法正确率高,速度快,具有很好的泛化能力和较强的鲁棒性,能够满足实时应用要求。
Fatigue driving may lead accident of battleplan and military vehicle to affect military logistic transportation. Fatigue may also lead negligence of the frontier defence duty to severe aftereffect. The fatigue detection technique which can detect the fatigue states and generates some warning alarms is considered as one of the most prospective applications of automatic facial expression recognition. Compared with the other detecting methods, the method based on machine-vision has many advantages such as: real-time response, veracity, non-contact, applicability, economy, which make it become the focus of current research.
Aiming at round-the-clock face detection, this paper proposes a face detecting algorithm using AdaBoost method. At first, the pupils are located using the red-eye effect of eyes in infrared illumination. The pupils’location is used to eliminate a majority of background. Then using AdaBoost method detects and locates face in the remanent region. This method simplifies the operation of traditional AdaBoost face detecting algorithm, and boosts the speed of face detection, benefits subsequent procedure, and improves the efficiency of the entire algorithm.
Aiming at the problem of the great influence of illumination and the low reliability, this paper proposes a fast fatigue detection algorithm based on AdaBoost. At first, the face is located in the infrared video image. Two sub-images which respectively contain mouth and eyes are segmented from face image. Then, the eyes’and mouth’s features which are extracted from the sub-images by PCA are classified by corresponding classifiers which are trained by AdaBoost earlier. Finally, the algorithm determines the state of the face is whether fatigued or not according to the output of the OR operation between the outputs of the classifiers. Proved by experiment, this algorithm has not only high correct rate and fast speed but also a powerful ability to generally use and kind of robustness. And the response time of this algorithm could satisfy the real-time requirement.
针对全天候、全时段人脸检测的要求,本文提出一种以红外光照环境下的人脸视频图像为输入,基于AdaBoost的方法人脸区域检测算法。该方法首先利用人眼在红外光照条件下的红眼效应定位瞳孔;然后根据瞳孔定位剔除大部分非人脸区域;最后在候选区域内使用AdaBoost方法完成对人脸区域的检测定位。该方法简化了传统AdaBoost人脸检测的运算,大幅提高了人脸检测速度。
针对以往疲劳检测算法普遍存在的受光照条件影响大、检测速度慢以及可靠性差的问题,本文提出一种基于AdaBoost的疲劳表情快速检测算法。该算法首先对输入的红外视频图像进行人脸检测定位,然后将眼睛、嘴巴分割出来,分别提取两个子图块的PCA特征,分别输入事先经过AdaBoost训练得到两个分类器进行判别,将两个分类器的输出进行或运算得到最终的检测结果。实验表明,本文算法正确率高,速度快,具有很好的泛化能力和较强的鲁棒性,能够满足实时应用要求。
Fatigue driving may lead accident of battleplan and military vehicle to affect military logistic transportation. Fatigue may also lead negligence of the frontier defence duty to severe aftereffect. The fatigue detection technique which can detect the fatigue states and generates some warning alarms is considered as one of the most prospective applications of automatic facial expression recognition. Compared with the other detecting methods, the method based on machine-vision has many advantages such as: real-time response, veracity, non-contact, applicability, economy, which make it become the focus of current research.
Aiming at round-the-clock face detection, this paper proposes a face detecting algorithm using AdaBoost method. At first, the pupils are located using the red-eye effect of eyes in infrared illumination. The pupils’location is used to eliminate a majority of background. Then using AdaBoost method detects and locates face in the remanent region. This method simplifies the operation of traditional AdaBoost face detecting algorithm, and boosts the speed of face detection, benefits subsequent procedure, and improves the efficiency of the entire algorithm.
Aiming at the problem of the great influence of illumination and the low reliability, this paper proposes a fast fatigue detection algorithm based on AdaBoost. At first, the face is located in the infrared video image. Two sub-images which respectively contain mouth and eyes are segmented from face image. Then, the eyes’and mouth’s features which are extracted from the sub-images by PCA are classified by corresponding classifiers which are trained by AdaBoost earlier. Finally, the algorithm determines the state of the face is whether fatigued or not according to the output of the OR operation between the outputs of the classifiers. Proved by experiment, this algorithm has not only high correct rate and fast speed but also a powerful ability to generally use and kind of robustness. And the response time of this algorithm could satisfy the real-time requirement.
引文
[1] NCSDR/NHTSA Expert Panel on Driver Fatigue & Sleepiness:Drowsy Driving and Automobile Crashes, Report HS 808707,1998.
[2] Gerry E, Warning system for fatigued drivers nearing reality with new eye data, Science Daily Magazine, 1999(7):25-30.
[3] C.J.Reissman,The Alert Driver: A Trucker’s Guide to Sleep, Fatigue, and Rest in our 24-Hours Society, American Trucking Associations, 2200 Mill Road, Alexandria USA 1996.
[4] 2008年全国道路交通事故概况,道路交通管理, 2009(1):4-5
[5] Wierwille, WW, Ellsworth, LA, Wreggit, SS, Fairbanks, RJ, Kirn, CL: Research on vehicle-based driver status/performance monitoring: development, validation, and refinement of algorithms for detection of driver drowsiness. National Highway Traffic Safety Administration Final Report: DOT HS 808 247, 1994.
[6] Federal Highway Administration, PERCLOS: A Valid Psychophysiological Measure of Alertness As Assessed by Psychomotor Vigilance. http://www.fmcsa.dot.gov .
[7] National Highway Traffic Safety Administration, Evaluation of Techniques for Ocular Measurement as an Index of Fatigue and the Basis for Alertness Management. http://www.fhwa.dot.gov .
[8]郑培,宋正河,周一鸣.机动车驾驶员驾驶疲劳测评方法的研究状况及发展趋势.中国农业大学学报. 2001,6(6): 101-105.
[9] Kithil Philip W., Jones Roger D., John MacCuish. Development of Driver Alertness Detection System Using Overhead Capacitive Sensor Array. SAE Technical Paper Series 982292, SAE International, 1998.
[10] Grace R., Benjamin A.L. Application of a Heavy Vehicle Drowsy Driver Detection System. Proceedings of the SAE International Truck & Bus Meeting and Exposition. November 15-17, 1999.
[11] Nikolaos P. Vision-based detection of driver fatigue. IEEE International Conference on Intelligent Transportation. 1997.
[12]王雪立.基于机器视觉的嵌入式驾驶疲劳检测系统研究.首都师范大学硕士论文. 2006.
[13]李琪,宋凯.疲劳驾驶预警装置的实现与软件设计.辽宁大学学报自然科学版, 2003, 30(4): 367-369.
[14]王荣本,郭克友,储江伟.一种基于Gabor小波的驾驶员眼部状态识别方法的研究.中国图象图形学报, 2003, 8A(9): 1043-1047.
[15]石坚,吴远鹏,卓斌等.汽车驾驶员主动安全性因素的辨识与分析.上海交通大学学报, 2000, 34(4): 441-444.
[16]陈艳琴.关于司机疲劳监测的人眼检测与跟踪研究.中南大学硕士学位论文. 2004.
[17]黎亚平,周杰,黄磊.国内外驾驶疲劳状态检测技术的现状与发展. 2009, 11, http://www.xici.net/main.asp?url=/u17966363/d103443492.htm
[18]周玉彬,俞梦孙.疲劳驾驶检测方法的研究[J].医疗卫生装备, 2003, (6): 25-28.
[19]郑培,宋正河,周一鸣.基于PERCLOS的机动车驾驶员驾驶疲劳的识别算法[J].中国农业大学学报. 2002, 7(2): 104-109.
[20]蔡笑岳.驾驶心理学[M].北京:中国劳动出版社, 1998.
[21] Freund Y., Schapire R. E. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 1997, 55(1): 119-139.
[22]宋春雷,王龙等.学习理论与鲁棒控制.控制理论与应用. Vol. 17(5), 633-636, 2000/10.
[23] Valiant L.G. A Theory of the Learnable. Communications of the ACM. Vol. 27(11), 1134-1142, 1984/11.
[24] Kearns M. The Computational Complexity of Machine Learning. Cambridge: MIT Press,1990.
[25] Kearns M., Valiant L.G. Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. Journal of the ACM, 41(1): 67-95,1994/01.
[26] Schapire R.E. The Strength of Weak Learnability. Machine Learning, 1990, 5(2): 197-227.
[27] Freund Y. Boosting a Weak Learning Algorithm by Majority. Information and Computation, 1995, 121(2): 256-285.
[28] Drucker H., Schapire R.E., Simard P. Boosting Performance in Neural Networks. International Journal of Pattern Recognition and Artificial Intelligence, 1993, 7(4): 705-719.
[29] Viola P., Jones M.J. Robust Real-time Face Detection. International Journal of computer Vision 57(2), 137-154, 2004.
[30] Yang Ming-Hsuan, David J.K., Narendra A. Detection Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 24(1): 34-58, 2002/01.
[31]李刚,高政.人脸自动识别方法综述.计算机研究. 2003, Vol. 8: 4-9.
[32] Rowley H.A., et al. Neural Network-Based Face Detection. IEEE Transactionson Pattern Analysis and Machine Intelligence. Vol. 20(1): 23-38, 1998/01.
[33] T. Kohonen, Self-Organization and Associative Memory, Springer 1989.
[34] Belhumeur P.N., Hespanha J.P., Kriegman D.J. Eigenfaces vs. Fisherface: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 19(7), 711-720, 1997.
[35] K.K. Sung, T. Poggio. Example-Based Learning for View-Based Human Face Detection. Technical Report AI Memo 1521, Massachusetts Inst. of Technology AI Lab, 1994.
[36] E. Osuna, R. Freund, F. Girosi. Training Support Vector Machines: An Application to Face Detection. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 130-136, 1997.
[37] B. Heiselet, T Serre, M Pontil et al. Component-based Face Detection. Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, 1: I657-I662.
[38] Nefian A., Hayes M. Hidden Markov Models for Face Recognition. IEEE International Conference on Acoustics. Speech and Signals Processings, Seattle, Washington, 1998: 2721-2724.
[39] Qiong Wang, Jingyu Yang, Mingwu Ren, Yujie Zheng. Driver Fatigue Detection: A Survey[C]. Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, June 21-23, 2006, 8587-8591.
[40] Kecklund G, Akerstedt T. Sleepiness in long distance truck driving: an ambulatory EEG study of night driving. Stockholm, Sweden: Department of Stress Research and National Institute for Psychosocial Factors and Karo1inska Institute. 1998,36(9):1007.
[41] S.K.L. Lal, A. Craig, P. Boord, L. Kirkup, and H. Nguyen,“Development of an algorithm for an EEG-based driver fatigue countermeasure,”Journal of Safety Research, pp. 321-328, 2003.
[42] Chrisms Papadelis, Chrysoula Kourtidou Papadeli, Panagiotis D.Bamidis, et a1 . Indicators of sleepiness in an ambulatory EEG study of night driving[A]. EMBS Annual International Conference[C].New York City,USA,2006.
[43]王炳浩,魏建勤,吴永红.汽车驾驶员瞌睡状态脑电波特征的初步探索.汽车工程, 2004, 26(1), 70-73.
[44]杨渝书,姚振强,李增勇,焦昆.心电图时频域指标在驾驶疲劳评价中的有效性研究.机械设计与制造, 2002(5), 94-95.
[45] Mengshoel AM et al. Muscle fatigue in early fibromyalgia. J Rheumatol.1995, 22: 143-150.
[46]曹倩霞,基于眼睑运动的司机疲劳检测,中南大学硕士学位论文. 2005.
[47] Philip W.K, Roger D.J. Development of Driver Alertness Detection System Using Overhead Capacitive Sensor Array. http://internet.cybermesa.com/~rfrye/complexica/drowsy.htm .
[48] Wylie CD, Shultz T, Miller JC et al. Commercial Motor Vehicle Driver Fatigue and Alertness Study, Technical Summery. Essex corporation, September, 1996. TP Report No.12876E.
[49] Haisong Gu, Qiang Ji, Zhiwei Zhu. Facial Tracking for Fatigue Detection. Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision(WACV’02). 2002.
[50] Sherry P., Bart S., Atwater J. Distractibility, fatigue, job stress, and accidents in long haul truck drivers. Paper presented at the annual.
[51] Grace R., Byrne V. E. Legrand J.M., Gricourt D.J, et al. A Machine Vision Based Drowsy Driver Detection System for Heavy Vehicles. Proceedings of The Ocular Measures of Driver Alertness Conference. 1999, 75-86.
[52] Ellen M.A, Grace R., Steinfeld A. A User-Centered Drowsy-Driver Detection and Warning System, Proceedings of ACM Designing User Experiences 2003.
[53] Wahlstrom E., Masoud O., Papanikolopoulos N., Vision-based methods for driver monitoring. Proceedings on Intelligent Transportation Systems, IEEE, 2003, Volume: 2, Pages: 903-908.
[54] Horng WB, Chen CY, Chang Y, Fan CH. Driver fatigue detection based on eye tracking and dynamic, template matching. IEEE International Conference on Networking, Sensing and Control. 2004, 1:7-12.
[55] Lois E.K, Hohu L.B, Michael H.S. Understanding of Irresistible Sleep. Mayo Clin Pro. 2001, 76: 185-194.
[56] Anneke Heitmann, Rainer Guttkuhn, Acacia Aguirre etc. Technologies for the Monitoring and Prevention of Driver Assessment, Training and Vehicle Design. 2001, 8: 81-86.
[57] Ji Q, Zhu ZW, Lan p. Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Transactions on Vehicular Technology. 2004, 53: 1052-1068.
[58] Gu HS, Ji Q, An automated face reader for fatigue detection. Sixth IEEE International Conference on Automatic Face and Gesture Recognition. 2004, 111-116.
[59]陈勇,黄琦,刘霞,张昌华.一种全天候驾驶员疲劳检测方法研究.仪器仪表学报, 2009第30卷第3期, 636-640.
[60] Zutao Zhang, Jiashu Zhang , A New Real-Time Eye Tracking for Driver Fatigue Detection, 2006 6th International Conference on ITS Telecommunications Proceedings, 8-11.
[61]张志刚,周明全,耿国华,人脸关键特征点自动标定研究,计算机工程与应用,. 2007年21期.
[2] Gerry E, Warning system for fatigued drivers nearing reality with new eye data, Science Daily Magazine, 1999(7):25-30.
[3] C.J.Reissman,The Alert Driver: A Trucker’s Guide to Sleep, Fatigue, and Rest in our 24-Hours Society, American Trucking Associations, 2200 Mill Road, Alexandria USA 1996.
[4] 2008年全国道路交通事故概况,道路交通管理, 2009(1):4-5
[5] Wierwille, WW, Ellsworth, LA, Wreggit, SS, Fairbanks, RJ, Kirn, CL: Research on vehicle-based driver status/performance monitoring: development, validation, and refinement of algorithms for detection of driver drowsiness. National Highway Traffic Safety Administration Final Report: DOT HS 808 247, 1994.
[6] Federal Highway Administration, PERCLOS: A Valid Psychophysiological Measure of Alertness As Assessed by Psychomotor Vigilance. http://www.fmcsa.dot.gov .
[7] National Highway Traffic Safety Administration, Evaluation of Techniques for Ocular Measurement as an Index of Fatigue and the Basis for Alertness Management. http://www.fhwa.dot.gov .
[8]郑培,宋正河,周一鸣.机动车驾驶员驾驶疲劳测评方法的研究状况及发展趋势.中国农业大学学报. 2001,6(6): 101-105.
[9] Kithil Philip W., Jones Roger D., John MacCuish. Development of Driver Alertness Detection System Using Overhead Capacitive Sensor Array. SAE Technical Paper Series 982292, SAE International, 1998.
[10] Grace R., Benjamin A.L. Application of a Heavy Vehicle Drowsy Driver Detection System. Proceedings of the SAE International Truck & Bus Meeting and Exposition. November 15-17, 1999.
[11] Nikolaos P. Vision-based detection of driver fatigue. IEEE International Conference on Intelligent Transportation. 1997.
[12]王雪立.基于机器视觉的嵌入式驾驶疲劳检测系统研究.首都师范大学硕士论文. 2006.
[13]李琪,宋凯.疲劳驾驶预警装置的实现与软件设计.辽宁大学学报自然科学版, 2003, 30(4): 367-369.
[14]王荣本,郭克友,储江伟.一种基于Gabor小波的驾驶员眼部状态识别方法的研究.中国图象图形学报, 2003, 8A(9): 1043-1047.
[15]石坚,吴远鹏,卓斌等.汽车驾驶员主动安全性因素的辨识与分析.上海交通大学学报, 2000, 34(4): 441-444.
[16]陈艳琴.关于司机疲劳监测的人眼检测与跟踪研究.中南大学硕士学位论文. 2004.
[17]黎亚平,周杰,黄磊.国内外驾驶疲劳状态检测技术的现状与发展. 2009, 11, http://www.xici.net/main.asp?url=/u17966363/d103443492.htm
[18]周玉彬,俞梦孙.疲劳驾驶检测方法的研究[J].医疗卫生装备, 2003, (6): 25-28.
[19]郑培,宋正河,周一鸣.基于PERCLOS的机动车驾驶员驾驶疲劳的识别算法[J].中国农业大学学报. 2002, 7(2): 104-109.
[20]蔡笑岳.驾驶心理学[M].北京:中国劳动出版社, 1998.
[21] Freund Y., Schapire R. E. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 1997, 55(1): 119-139.
[22]宋春雷,王龙等.学习理论与鲁棒控制.控制理论与应用. Vol. 17(5), 633-636, 2000/10.
[23] Valiant L.G. A Theory of the Learnable. Communications of the ACM. Vol. 27(11), 1134-1142, 1984/11.
[24] Kearns M. The Computational Complexity of Machine Learning. Cambridge: MIT Press,1990.
[25] Kearns M., Valiant L.G. Cryptographic Limitations on Learning Boolean Formulae and Finite Automata. Journal of the ACM, 41(1): 67-95,1994/01.
[26] Schapire R.E. The Strength of Weak Learnability. Machine Learning, 1990, 5(2): 197-227.
[27] Freund Y. Boosting a Weak Learning Algorithm by Majority. Information and Computation, 1995, 121(2): 256-285.
[28] Drucker H., Schapire R.E., Simard P. Boosting Performance in Neural Networks. International Journal of Pattern Recognition and Artificial Intelligence, 1993, 7(4): 705-719.
[29] Viola P., Jones M.J. Robust Real-time Face Detection. International Journal of computer Vision 57(2), 137-154, 2004.
[30] Yang Ming-Hsuan, David J.K., Narendra A. Detection Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 24(1): 34-58, 2002/01.
[31]李刚,高政.人脸自动识别方法综述.计算机研究. 2003, Vol. 8: 4-9.
[32] Rowley H.A., et al. Neural Network-Based Face Detection. IEEE Transactionson Pattern Analysis and Machine Intelligence. Vol. 20(1): 23-38, 1998/01.
[33] T. Kohonen, Self-Organization and Associative Memory, Springer 1989.
[34] Belhumeur P.N., Hespanha J.P., Kriegman D.J. Eigenfaces vs. Fisherface: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 19(7), 711-720, 1997.
[35] K.K. Sung, T. Poggio. Example-Based Learning for View-Based Human Face Detection. Technical Report AI Memo 1521, Massachusetts Inst. of Technology AI Lab, 1994.
[36] E. Osuna, R. Freund, F. Girosi. Training Support Vector Machines: An Application to Face Detection. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 130-136, 1997.
[37] B. Heiselet, T Serre, M Pontil et al. Component-based Face Detection. Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, 1: I657-I662.
[38] Nefian A., Hayes M. Hidden Markov Models for Face Recognition. IEEE International Conference on Acoustics. Speech and Signals Processings, Seattle, Washington, 1998: 2721-2724.
[39] Qiong Wang, Jingyu Yang, Mingwu Ren, Yujie Zheng. Driver Fatigue Detection: A Survey[C]. Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, June 21-23, 2006, 8587-8591.
[40] Kecklund G, Akerstedt T. Sleepiness in long distance truck driving: an ambulatory EEG study of night driving. Stockholm, Sweden: Department of Stress Research and National Institute for Psychosocial Factors and Karo1inska Institute. 1998,36(9):1007.
[41] S.K.L. Lal, A. Craig, P. Boord, L. Kirkup, and H. Nguyen,“Development of an algorithm for an EEG-based driver fatigue countermeasure,”Journal of Safety Research, pp. 321-328, 2003.
[42] Chrisms Papadelis, Chrysoula Kourtidou Papadeli, Panagiotis D.Bamidis, et a1 . Indicators of sleepiness in an ambulatory EEG study of night driving[A]. EMBS Annual International Conference[C].New York City,USA,2006.
[43]王炳浩,魏建勤,吴永红.汽车驾驶员瞌睡状态脑电波特征的初步探索.汽车工程, 2004, 26(1), 70-73.
[44]杨渝书,姚振强,李增勇,焦昆.心电图时频域指标在驾驶疲劳评价中的有效性研究.机械设计与制造, 2002(5), 94-95.
[45] Mengshoel AM et al. Muscle fatigue in early fibromyalgia. J Rheumatol.1995, 22: 143-150.
[46]曹倩霞,基于眼睑运动的司机疲劳检测,中南大学硕士学位论文. 2005.
[47] Philip W.K, Roger D.J. Development of Driver Alertness Detection System Using Overhead Capacitive Sensor Array. http://internet.cybermesa.com/~rfrye/complexica/drowsy.htm .
[48] Wylie CD, Shultz T, Miller JC et al. Commercial Motor Vehicle Driver Fatigue and Alertness Study, Technical Summery. Essex corporation, September, 1996. TP Report No.12876E.
[49] Haisong Gu, Qiang Ji, Zhiwei Zhu. Facial Tracking for Fatigue Detection. Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision(WACV’02). 2002.
[50] Sherry P., Bart S., Atwater J. Distractibility, fatigue, job stress, and accidents in long haul truck drivers. Paper presented at the annual.
[51] Grace R., Byrne V. E. Legrand J.M., Gricourt D.J, et al. A Machine Vision Based Drowsy Driver Detection System for Heavy Vehicles. Proceedings of The Ocular Measures of Driver Alertness Conference. 1999, 75-86.
[52] Ellen M.A, Grace R., Steinfeld A. A User-Centered Drowsy-Driver Detection and Warning System, Proceedings of ACM Designing User Experiences 2003.
[53] Wahlstrom E., Masoud O., Papanikolopoulos N., Vision-based methods for driver monitoring. Proceedings on Intelligent Transportation Systems, IEEE, 2003, Volume: 2, Pages: 903-908.
[54] Horng WB, Chen CY, Chang Y, Fan CH. Driver fatigue detection based on eye tracking and dynamic, template matching. IEEE International Conference on Networking, Sensing and Control. 2004, 1:7-12.
[55] Lois E.K, Hohu L.B, Michael H.S. Understanding of Irresistible Sleep. Mayo Clin Pro. 2001, 76: 185-194.
[56] Anneke Heitmann, Rainer Guttkuhn, Acacia Aguirre etc. Technologies for the Monitoring and Prevention of Driver Assessment, Training and Vehicle Design. 2001, 8: 81-86.
[57] Ji Q, Zhu ZW, Lan p. Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Transactions on Vehicular Technology. 2004, 53: 1052-1068.
[58] Gu HS, Ji Q, An automated face reader for fatigue detection. Sixth IEEE International Conference on Automatic Face and Gesture Recognition. 2004, 111-116.
[59]陈勇,黄琦,刘霞,张昌华.一种全天候驾驶员疲劳检测方法研究.仪器仪表学报, 2009第30卷第3期, 636-640.
[60] Zutao Zhang, Jiashu Zhang , A New Real-Time Eye Tracking for Driver Fatigue Detection, 2006 6th International Conference on ITS Telecommunications Proceedings, 8-11.
[61]张志刚,周明全,耿国华,人脸关键特征点自动标定研究,计算机工程与应用,. 2007年21期.