基于人脸面部特征的驾驶员疲劳检测技术研究
|
摘要
随着交通事业的迅猛发展,交通安全已成为全球范围内普遍关注的重要问题。然而,每年因交通事故丧失生命的人数依然居高不下,各类恶性交通事故仍然接连发生,经研究发现,其中因驾驶员疲劳驾驶造成的事故占相当大的比例,由此可见,疲劳驾驶已成为伤害人身安全的一大隐患。因而,驾驶员疲劳状态检测研究及预警系统设计成为近几年国内外研究的热点。
针对该问题,本文从驾驶员面部特征入手,通过识别眼部及嘴唇的状态判断驾驶员是否发生疲劳,并给予及时预警。本文主要工作如下:
首先,讨论并研究了一种多姿态环境下基于局部Gabor方向投影子空间的多状态模板人眼定位方法。该方法首先采用AdaBoost人脸检测算法从输入的彩色图像序列中得到人脸区域,并通过光照补偿对人脸进行预处理,以消除光照不均对实验效果的影响,再依据人脸结构特征,对人脸头部姿势进行估计和校正,最后,利用多尺度水平方向Gabor变换后的人脸图像在眉眼处幅值较大的特点,通过局部投影方法得到左右眉眼的大致区域,并在此区域中采用多状态模板匹配进行人眼精确定位。
其次,在对传统Mean Shift跟踪算法分析研究的基础上,对其进行改进,采用人眼颜色特征与局部二值模式(LBP)纹理特征相结合的表示方法对人眼进行描述,并将其成功嵌入Mean Shift算法实现人眼跟踪。
最后,利用Canny算子对人眼进行边缘提取,通过测量上下眼睑距离进行眨眼检测,再利用唇色信息在人脸图像中找出嘴唇部位,根据嘴唇宽高比描述嘴唇的张开程度,通过打哈欠检测与PERCLOS原理相结合判断驾驶员是否发生疲劳。经实验验证,本文提出的方法有效可行,对不同光照及人脸姿态下的疲劳状态实时检测及跟踪具有较高的鲁棒性。
With the rapid development of transport, the traffic safety has become an important issue be commonly concerned on a global scale. However, each year the figure of people killed in traffic accidents is remaining obstinately high, and all kinds of vicious traffic accidents are still occur. Study shows that most of them are caused by the drivers’fatigue, so driver fatigue has become a hazard to personal safety. Therefore, the study and design of safety assist systems specific to driver fatigue driving attracted much attention of researchers, and become a hotspot all over the world.
For the problem, the paper select the facial features of driver as the judgement method to detect the driver fatigue and give timely warning. The main work done in this paper is as follows:
Firstly, a method of eye localization and detection based on multimode template matching in projection subspace of local gabor derection under multi-pose condition is presented. The face region is obtained from input color image by AdaBoost algorithm of face detection firstly; Secondly,lighting compensation in face region is done to eliminate the effects of illumination,then the lip is detected according to the lips color, next the left and right corners are detected by harris corner detection algorithm, and the perpendicular bisector of the corners is the symmetry of the face, then the deflection angle of the face is estimated. Finally, gabor transform is used in the corrected face, and local gray projection is performed to locate the rough areas of eyes, then using the multimode template matching in the areas to locate the eyes and judge the state of eyes.
Secondly, by analyzing the traditional Mean Shift algorithm, a new approach based on LBP descriptor embedded Mean Shift algorithm for eye and mouth fatigue state tracking is presented in the thesis. The texture features of local binary pattern (LBP) and the color features are combined with together effectively to describe the human eye and embeded Mean Shift algorithm for realizing eye tracking.
Finally, the edge image is got by Canny operator from the original image of eye, and then we detecte the blink by measuring the distance of the upper and lower eyelids, next the lip is detected according to the lips color, and the state of mouth is described by the aspect ratio of the lip. In order to make the algorithm more valid, we combined PERCLOS with yawning detection to detect driver’s drowsy state, if yawning is detected at the same time, the driver will be judged to fatigue. Experimental results show that the proposed algorithm is effective and feasible, and robust to variations of background, illumination and pose.
针对该问题,本文从驾驶员面部特征入手,通过识别眼部及嘴唇的状态判断驾驶员是否发生疲劳,并给予及时预警。本文主要工作如下:
首先,讨论并研究了一种多姿态环境下基于局部Gabor方向投影子空间的多状态模板人眼定位方法。该方法首先采用AdaBoost人脸检测算法从输入的彩色图像序列中得到人脸区域,并通过光照补偿对人脸进行预处理,以消除光照不均对实验效果的影响,再依据人脸结构特征,对人脸头部姿势进行估计和校正,最后,利用多尺度水平方向Gabor变换后的人脸图像在眉眼处幅值较大的特点,通过局部投影方法得到左右眉眼的大致区域,并在此区域中采用多状态模板匹配进行人眼精确定位。
其次,在对传统Mean Shift跟踪算法分析研究的基础上,对其进行改进,采用人眼颜色特征与局部二值模式(LBP)纹理特征相结合的表示方法对人眼进行描述,并将其成功嵌入Mean Shift算法实现人眼跟踪。
最后,利用Canny算子对人眼进行边缘提取,通过测量上下眼睑距离进行眨眼检测,再利用唇色信息在人脸图像中找出嘴唇部位,根据嘴唇宽高比描述嘴唇的张开程度,通过打哈欠检测与PERCLOS原理相结合判断驾驶员是否发生疲劳。经实验验证,本文提出的方法有效可行,对不同光照及人脸姿态下的疲劳状态实时检测及跟踪具有较高的鲁棒性。
With the rapid development of transport, the traffic safety has become an important issue be commonly concerned on a global scale. However, each year the figure of people killed in traffic accidents is remaining obstinately high, and all kinds of vicious traffic accidents are still occur. Study shows that most of them are caused by the drivers’fatigue, so driver fatigue has become a hazard to personal safety. Therefore, the study and design of safety assist systems specific to driver fatigue driving attracted much attention of researchers, and become a hotspot all over the world.
For the problem, the paper select the facial features of driver as the judgement method to detect the driver fatigue and give timely warning. The main work done in this paper is as follows:
Firstly, a method of eye localization and detection based on multimode template matching in projection subspace of local gabor derection under multi-pose condition is presented. The face region is obtained from input color image by AdaBoost algorithm of face detection firstly; Secondly,lighting compensation in face region is done to eliminate the effects of illumination,then the lip is detected according to the lips color, next the left and right corners are detected by harris corner detection algorithm, and the perpendicular bisector of the corners is the symmetry of the face, then the deflection angle of the face is estimated. Finally, gabor transform is used in the corrected face, and local gray projection is performed to locate the rough areas of eyes, then using the multimode template matching in the areas to locate the eyes and judge the state of eyes.
Secondly, by analyzing the traditional Mean Shift algorithm, a new approach based on LBP descriptor embedded Mean Shift algorithm for eye and mouth fatigue state tracking is presented in the thesis. The texture features of local binary pattern (LBP) and the color features are combined with together effectively to describe the human eye and embeded Mean Shift algorithm for realizing eye tracking.
Finally, the edge image is got by Canny operator from the original image of eye, and then we detecte the blink by measuring the distance of the upper and lower eyelids, next the lip is detected according to the lips color, and the state of mouth is described by the aspect ratio of the lip. In order to make the algorithm more valid, we combined PERCLOS with yawning detection to detect driver’s drowsy state, if yawning is detected at the same time, the driver will be judged to fatigue. Experimental results show that the proposed algorithm is effective and feasible, and robust to variations of background, illumination and pose.
引文
1陈勇,黄琦,刘霞,等.一种全天候驾驶员疲劳检测方法研究.仪器仪表学报, 2009, 30(3):636-640
2 K. L. Lal Saroj, Ashley Craig. Electroencephalography Activity Associated with Driver Fatigue: Implications for a Fatigue Counter Measure Device. Journal of Psychophysiology, 2001,15(3):183-189
3 S. K. Lal, A. Craig, P. Boord, et al. Development of an Algorithm for an EEG-based Driver Fatigue Counter Measure. Journal of Safety Research, 2003,34(3):321-328
4 Haisong Gu, Qiang Ji. An Antomated Face Reader for Fatigue Detection. IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, 2004:111-116
5 Budi Thomas Jap, Sara Lal, Peter Fischer, et al. Using EEG Spectral Components to Assess Alogrithms for Detecting Fatigue. Expert Systems with Applications, 2009,36(2): 2352-2359
6 Mingwang Mao, Liping Du. Research on Drive Fatigue Detection Using Wavelet Transform. IEEE International Conference on Vehicular Electronics and Safety, Beijing, China, 2007:1-4
7 Kai-Quan Shen, Xiao-Ping Li, Chong-Jin Ong, et al. EEG-based Mental Fatigue Measurement Using Multi-class Support Vector Machines with Confidence Estimate. Clinical Neurophysiology, 2008,119(7):1524-1533
8 V. M. Yeo Mervyn, Li Xiaoping, Kaiquan Shen, et al. Can SVM Be Used for Automatic EEG Detection of Drowsiness during Car Driving? Safety Science, 2009,47(1):115-124
9 Mieko Ohsuga, Yoshiyuki Kamakura, Yumiko Inoue, et al. Classification of Blink Waveforms Toward the Assessment of Driver’s Arousal Levels—An EOG Approach and the Correlation with Physiological Measures. Proceedings of the 7th International Conference on Engineering Psychology and Cognitive Ergonomics, Beijing, China, 2007:787-795
10 Yoshihiro Noguchi, Roongroj Nopsuwanchai, Mieko Ohsuga. Classification of Blink Waveforms Towards the Assessment of Driver’s Arousal Level—An Approach for HMM Based Classification from Blinking Video Sequence. Proceedings of the 7th International Conference on Engineering Psychology and Cognitive Ergonomics, Beijing, China, 2007:779-786
11 E. Rogado, J. L. Garcia, R. Barea. Driver Fatigue Detection System. IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, 2009:1105-1110
12 T. Matsuda, M. Makikawa. ECG Monitoring of A Car Driver Using Capactively Coupled Electrodes. IEEE International Conference on Engineering in Medicine and Biology Society, Vancouver, BC, 2008:1315-1318
13 Fan Xiao, Sun Yanfeng, Yin Baocai, et al. Gabor-based Dynamic Representation for Human Fatigue Monitoring in Facial Image Sequences. Pattern Recognition Letters, 2010,31(3):234-243
14 Yang Guosheng, Lin Yingzi, Prabir Bhattacharya. A Driver Fatigue Recognition Model Based on Information Fusion and Dynamic Bayesian Network. Information Sciences, 2010,180(10):1942-1954
15 Wang Rongben, Guo Lie, Tong Bingliang, et al. Monitoring Method Movement for Driver Fatigue or Distraction with One Camera. IEEE International Conference on Intelligent Transportation Systems, Washington, DC, United states, 2005:314-319
16 Shi Pengfei, Wang Tiesheng. Yawning Detection for Determining Driver Drowsiness. IEEE International Conference on Workshop on VLSI Design and Video Technology, Suzhou, China, 2005:373-376
17 Lu Yufeng, Wang Zengcai. Detecting Driver Yawning in Successive Images. IEEE International Conference on Bioinformatics and Biomedical Engineering, Wuhan, China, 2007:581-583
18 Anneke Heitmann, Rainer Guttkuhn, Acacia Aguitte, et al. Technologies for the Mnitoring and Prevention of Driver Fatigue. Proceedings of the First International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Lexington, USA, 2001:81-86
19 Yuan Jinghe. Eye Location under Different Eye Poses, Scales, Illuminations. Chinese Optics Letters, 2010,8(1):59-62
20 A. Geetha, V. Ramalingam, S. Palanivel, et al. Facial Expression Recognition-A Real Time Approach. Expert Systems with Applications, 2009,36(1):303-308
21 Lu Ling, Yang Yong, Wang Lei, et al. Eye Location Based on Gray Projection. Third International Symposium on Intelligent Information Technology Application, Nan Chang, China, 2009:58-60
22 Mohammad Hossein Khosravi, Reza Safabakhsh. Human Eye Sclera Detection and Tracking Using a Modified Time-adaptive Self-organizing Map. Pattern Recognition, 2008,41(4):2571-2593
23 Ni Fei, Fu Zhang, Cao Qixin, et al. Image Processing Method for Eyes Location Based on Segmentation texture. Sensors and Actuators, 2008,143(2):439-451
24 Ioana Bacivarov, Mircea Ionita. Statistical Models of Appearance for Eye Tracking and Eye-blink Detection and Measurement. IEEE Trans on Consumer Electronics, 2008,54(3):1312-1320
25 Chiunhsiun Lin. Face Detection in Complicated Backgrounds and Different Illumination Conditions by Using YCbCr Color Space and Neural Network. Pattern Recognition Letters, 2007,28(16):2190-2200
26 Sun Hungming. Skin Detectioon for Single Images Using Dynamic Skin Color Modeling. Pattern Recognition, 2010,43(4):1413-1420
27 Zhong Jin, Zhen Lou, Yang Jingyu, et al. Face Detectioon Using Template Matching and Skin-color Information. Neurocomputing, 2007,70(4):794-800
28 Jiang Qiangrong, Li Hualan. Robust Human Face Detection in Complicated Color Images. IEEE International Conference on Information Management and Engineering, Chengdu, China, 2010,1:218-221
29 El Bakry, M. Hazem. New Fast Principal Component Analysis for Real-time Face Detection. Machine Graphics and Vision, 2009,18(4):405-425
30 Heisele Bernd. A Component-based Framework for Face Detection and Identification. International Journal of Computer Vision, 2007,74(2):167-181
31 Li Ying, Lai Jianhuang, Ruen Pongchi. Muti-template ASM and Its Application in Facial Feature Points Detection. Computer Research and Development, 2007,44(1):133-140
32 Ge Xinliang, Yang Jie, Zheng Zhonglong, et al. Multi-view Based Face Chin Contour Extraction. Engineering Applications of Artificial Intelligence, 2006,19(5):545-555
33 Fan Xiaojiu, Peng Qiang, Chen Jim X, et al. An Improved AAM Fast Localization Method for Human Facial Features. Journal of Electronics and Information Technology, 2009,31(6):1354-1358
34 U. Batur Aziz, H. Hayes Monson. Adaptive Active Appearance Models. IEEE Trans on Image Processing, 2005,14(11):1707-1721
35 Paul Viola, Michael Jones. Robust Real-time Object Detection. International Journal of Computer Vision, 2004,57(2):137-154
36 Y. Araki, N. Shimada, Y. Shirai. Detection of Faces of Various Directions in Complex Backgrounds. 16th International Conference on Pattern Recognition, QC, Canada, 2002:409-412
37张小洪,李博,杨丹.一种新的Harris多尺度角点检测.电子与信息学报, 2007,29(7): 1735-1738
38闫龙,赵正旭,周以齐.图像质量对Harris角点检测的影响研究.山东大学学报,2006,
36(5):21-24
39 Zhangqiang Zheng, Han Wang, Eam Khwang Teoh. Analysis of Gray Level Corner Detection. Pattern Recognition Letters, 1999,20(2):149-162
40 Horng Wen-Bing, Chen Chih-Yuan. A Real-time Driver Fatigue Detection System Based on Eye Tracking and Dynamic Template Matching. Tamkang Journal of Science and Engineering, 2008,11(1):65-72
41邬大鹏,程卫平,于盛林.基于帧间差分和运动估计的Camshift目标跟踪算法.光电工程,2010,37(1):55-60
42张昊,黄战华,郁道银,等.基于差分图像的运动目标跟踪与分割方法的应用研究.光学技术,2005,31(4):565-567
43 Wang Zhaowen, Yang Xiaokang, Xu Yi, et al. Camshift Guided Particle Filter for Visual Tracking. Pattern Recognition Letters, 2009,30(4):407-413
44 Li Yali, Wang Shengjin. Eye/eyes Tracking Based on a Unified Deformable Template and Particle Filtering. Parrern Recognition Letters, 2010,31(11):1377-1387
45 Zhang Zutao, Zhang, Jiashu. A New Real-time Eye Tracking Based on Nonlinear Unscented Kalman Filter for Monitoring Driver Fatigue. Journal of Control Theory and Applications, 2010,8(2):181-188
46 Zhang Tao, Cai Canhui. A Face Tracking Algorithm Based on Multiple Feature Mean Shift. Journal of Electronics and Information Technology, 2009,31(8):1816-1820
47 Li Shuxiao, Chang Hongxing, Zhu Chengfei. Adaptive Pyramid Mean Shift for Global Real-time Visual Tracking. Image and Vision Computing, 2010,28(3):424-437
48 Ido Leichter, Michael Lindenbaum, Ehud Rivlin. Mean Shift Tracking with Multiple Reference Color Histograms. Computer Vision and Image Understanding, 2010,114(3): 400-408
49王新红,王晶,田敏.基于空间边缘方向直方图的Mean Shift跟踪算法.中国图象图形学报,2008,13(3):586-592
50朱胜利,朱善安.核函数带宽自适应的Mean Shift目标跟踪算法.光电工程,2006, 33(8):11-16
51张恒,李立春,于起峰.尺度方向自适应Mean Shift跟踪算法.光学精密工程,2008, 16(6):1133-1139
52左军毅,梁彦,赵春晖,等.一种新的基于Mean Shift的目标三自由度跟踪算法.电子与信息学报,2008,30(1):172-175
53 Marko Heikkila, Matti Pietikainen. A Texture-based Method for Modeling the Background and Detecting Moving Objects. IEEE Trans on Pattern Analysis and Machine Intelligence, 2006,28(4):657-662
54郑永斌,黄新生,丰松江.SIFT和旋转不变LBP相结合的图像匹配算法.计算机辅助设计与图形学学报,2010,22(2):286-292
55应自炉,蔡淋波,刘召义.基于LBP的拉普拉斯特征映射人脸识别.信号处理,2010,26(8):1230-1233
56 Ning Jifeng, Wu Chengke. Robust Object Tracking Using Joint Color-texture Histogram. Intenationgal Journal of Pattern Recognition and Artificial Intelligence , 2009,23(7):1245-1263
57 F. Friedrichs.Camera-based Drowsiness Reference for Driver State Classification under Real Driving Conditions. IEEE International Conference on Intelligent Vehicles Symposium, La Jolla, CA, United states, 2010:101-106
58朱振华,吴晓娟,王磊,等.基于眨眼持续时间的司机疲劳检测方法.计算机工程,2008:34(5):201-206
59 P. Smith, M. Shah, N. V. Lobo. Determining Driver Visual Attention with One Camera. IEEE Trans on Intelligent Transportation Systems, 2003,4(4):205-218
60 Shanshan Zhang, Fuqiang Liu, Zhipeng Li. An Effective Driver Fatigue Monitoring System. IEEE International Conference on Machine Vision and Human-machine Interface, Kaifeng, China, 2010: 279-282
61 M. Bergasa Luis, Jesus Nuevo, Miguel A. Sotelo, Rafael Barea, et al. Real-time System for Monitoring Driver Vigilance. IEEE Trans on Intelligent Transportation Systems, 2006,7(1):63-77
62 Fan Xiao, Yin Baocai, Sun Yanfeng. Yawning Detection for Monitoring Driver Fatigue. Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, China, 2007,2:664-668
63 Li Lingling, Chen Yangzhou, Li Zhenglong. Yawning Detection for Monitoring Driver Fatigue Based on Two Cameras. IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, United states, 2009:12-17
64 Azim Tayyaba, M. Arfan. Jaffar, Mirza Anwar Majid. Automatic Fatigue Detection of Drivers Through Pupil Detection and Yawning Analysis. 4th International Conference on Innovative Computing, Information and Control, Kaohsiung, Taiwan, 2009:441-445
65 Azim Tayyaba, M. Arfan. Jaffar, Mirza Anwar Majid. Real Time Fatigue Detection of Drivers Through Eye Closure Duration And Yawning Analysis. ICIC Express Letters, 2010,4(3):725-731
66 Chu Jiangwei, Jin Lisheng, Tong Binliang, et al. A Monitoring Method of Driver Mouth Behavior Based on Machine Vision. IEEE International Conference on Intelligent Vehicles Symposiuim, Parma, Italy, 2004:351-356
67 F. Torre Dela. Subspace Eyetracking for Driver Warning. IEEE International Conference on Image Processing, Barcelona, Spain, 2003,3:329-332
68 A. Caldwell John, J. Lynn Caldwell, M. Schmidt Regina. Alertness Management Strategies for Operational Contexts. Sleep Medicine Reviews, 2008,12(4):257-273
69 A. V. Desai, M. A. Haque. Vigilance monitoring for Operator Safety: A Simulation Study on Highway Driving. Journal of Safety Research, 2006,37(2):139-147
2 K. L. Lal Saroj, Ashley Craig. Electroencephalography Activity Associated with Driver Fatigue: Implications for a Fatigue Counter Measure Device. Journal of Psychophysiology, 2001,15(3):183-189
3 S. K. Lal, A. Craig, P. Boord, et al. Development of an Algorithm for an EEG-based Driver Fatigue Counter Measure. Journal of Safety Research, 2003,34(3):321-328
4 Haisong Gu, Qiang Ji. An Antomated Face Reader for Fatigue Detection. IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, 2004:111-116
5 Budi Thomas Jap, Sara Lal, Peter Fischer, et al. Using EEG Spectral Components to Assess Alogrithms for Detecting Fatigue. Expert Systems with Applications, 2009,36(2): 2352-2359
6 Mingwang Mao, Liping Du. Research on Drive Fatigue Detection Using Wavelet Transform. IEEE International Conference on Vehicular Electronics and Safety, Beijing, China, 2007:1-4
7 Kai-Quan Shen, Xiao-Ping Li, Chong-Jin Ong, et al. EEG-based Mental Fatigue Measurement Using Multi-class Support Vector Machines with Confidence Estimate. Clinical Neurophysiology, 2008,119(7):1524-1533
8 V. M. Yeo Mervyn, Li Xiaoping, Kaiquan Shen, et al. Can SVM Be Used for Automatic EEG Detection of Drowsiness during Car Driving? Safety Science, 2009,47(1):115-124
9 Mieko Ohsuga, Yoshiyuki Kamakura, Yumiko Inoue, et al. Classification of Blink Waveforms Toward the Assessment of Driver’s Arousal Levels—An EOG Approach and the Correlation with Physiological Measures. Proceedings of the 7th International Conference on Engineering Psychology and Cognitive Ergonomics, Beijing, China, 2007:787-795
10 Yoshihiro Noguchi, Roongroj Nopsuwanchai, Mieko Ohsuga. Classification of Blink Waveforms Towards the Assessment of Driver’s Arousal Level—An Approach for HMM Based Classification from Blinking Video Sequence. Proceedings of the 7th International Conference on Engineering Psychology and Cognitive Ergonomics, Beijing, China, 2007:779-786
11 E. Rogado, J. L. Garcia, R. Barea. Driver Fatigue Detection System. IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, 2009:1105-1110
12 T. Matsuda, M. Makikawa. ECG Monitoring of A Car Driver Using Capactively Coupled Electrodes. IEEE International Conference on Engineering in Medicine and Biology Society, Vancouver, BC, 2008:1315-1318
13 Fan Xiao, Sun Yanfeng, Yin Baocai, et al. Gabor-based Dynamic Representation for Human Fatigue Monitoring in Facial Image Sequences. Pattern Recognition Letters, 2010,31(3):234-243
14 Yang Guosheng, Lin Yingzi, Prabir Bhattacharya. A Driver Fatigue Recognition Model Based on Information Fusion and Dynamic Bayesian Network. Information Sciences, 2010,180(10):1942-1954
15 Wang Rongben, Guo Lie, Tong Bingliang, et al. Monitoring Method Movement for Driver Fatigue or Distraction with One Camera. IEEE International Conference on Intelligent Transportation Systems, Washington, DC, United states, 2005:314-319
16 Shi Pengfei, Wang Tiesheng. Yawning Detection for Determining Driver Drowsiness. IEEE International Conference on Workshop on VLSI Design and Video Technology, Suzhou, China, 2005:373-376
17 Lu Yufeng, Wang Zengcai. Detecting Driver Yawning in Successive Images. IEEE International Conference on Bioinformatics and Biomedical Engineering, Wuhan, China, 2007:581-583
18 Anneke Heitmann, Rainer Guttkuhn, Acacia Aguitte, et al. Technologies for the Mnitoring and Prevention of Driver Fatigue. Proceedings of the First International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Lexington, USA, 2001:81-86
19 Yuan Jinghe. Eye Location under Different Eye Poses, Scales, Illuminations. Chinese Optics Letters, 2010,8(1):59-62
20 A. Geetha, V. Ramalingam, S. Palanivel, et al. Facial Expression Recognition-A Real Time Approach. Expert Systems with Applications, 2009,36(1):303-308
21 Lu Ling, Yang Yong, Wang Lei, et al. Eye Location Based on Gray Projection. Third International Symposium on Intelligent Information Technology Application, Nan Chang, China, 2009:58-60
22 Mohammad Hossein Khosravi, Reza Safabakhsh. Human Eye Sclera Detection and Tracking Using a Modified Time-adaptive Self-organizing Map. Pattern Recognition, 2008,41(4):2571-2593
23 Ni Fei, Fu Zhang, Cao Qixin, et al. Image Processing Method for Eyes Location Based on Segmentation texture. Sensors and Actuators, 2008,143(2):439-451
24 Ioana Bacivarov, Mircea Ionita. Statistical Models of Appearance for Eye Tracking and Eye-blink Detection and Measurement. IEEE Trans on Consumer Electronics, 2008,54(3):1312-1320
25 Chiunhsiun Lin. Face Detection in Complicated Backgrounds and Different Illumination Conditions by Using YCbCr Color Space and Neural Network. Pattern Recognition Letters, 2007,28(16):2190-2200
26 Sun Hungming. Skin Detectioon for Single Images Using Dynamic Skin Color Modeling. Pattern Recognition, 2010,43(4):1413-1420
27 Zhong Jin, Zhen Lou, Yang Jingyu, et al. Face Detectioon Using Template Matching and Skin-color Information. Neurocomputing, 2007,70(4):794-800
28 Jiang Qiangrong, Li Hualan. Robust Human Face Detection in Complicated Color Images. IEEE International Conference on Information Management and Engineering, Chengdu, China, 2010,1:218-221
29 El Bakry, M. Hazem. New Fast Principal Component Analysis for Real-time Face Detection. Machine Graphics and Vision, 2009,18(4):405-425
30 Heisele Bernd. A Component-based Framework for Face Detection and Identification. International Journal of Computer Vision, 2007,74(2):167-181
31 Li Ying, Lai Jianhuang, Ruen Pongchi. Muti-template ASM and Its Application in Facial Feature Points Detection. Computer Research and Development, 2007,44(1):133-140
32 Ge Xinliang, Yang Jie, Zheng Zhonglong, et al. Multi-view Based Face Chin Contour Extraction. Engineering Applications of Artificial Intelligence, 2006,19(5):545-555
33 Fan Xiaojiu, Peng Qiang, Chen Jim X, et al. An Improved AAM Fast Localization Method for Human Facial Features. Journal of Electronics and Information Technology, 2009,31(6):1354-1358
34 U. Batur Aziz, H. Hayes Monson. Adaptive Active Appearance Models. IEEE Trans on Image Processing, 2005,14(11):1707-1721
35 Paul Viola, Michael Jones. Robust Real-time Object Detection. International Journal of Computer Vision, 2004,57(2):137-154
36 Y. Araki, N. Shimada, Y. Shirai. Detection of Faces of Various Directions in Complex Backgrounds. 16th International Conference on Pattern Recognition, QC, Canada, 2002:409-412
37张小洪,李博,杨丹.一种新的Harris多尺度角点检测.电子与信息学报, 2007,29(7): 1735-1738
38闫龙,赵正旭,周以齐.图像质量对Harris角点检测的影响研究.山东大学学报,2006,
36(5):21-24
39 Zhangqiang Zheng, Han Wang, Eam Khwang Teoh. Analysis of Gray Level Corner Detection. Pattern Recognition Letters, 1999,20(2):149-162
40 Horng Wen-Bing, Chen Chih-Yuan. A Real-time Driver Fatigue Detection System Based on Eye Tracking and Dynamic Template Matching. Tamkang Journal of Science and Engineering, 2008,11(1):65-72
41邬大鹏,程卫平,于盛林.基于帧间差分和运动估计的Camshift目标跟踪算法.光电工程,2010,37(1):55-60
42张昊,黄战华,郁道银,等.基于差分图像的运动目标跟踪与分割方法的应用研究.光学技术,2005,31(4):565-567
43 Wang Zhaowen, Yang Xiaokang, Xu Yi, et al. Camshift Guided Particle Filter for Visual Tracking. Pattern Recognition Letters, 2009,30(4):407-413
44 Li Yali, Wang Shengjin. Eye/eyes Tracking Based on a Unified Deformable Template and Particle Filtering. Parrern Recognition Letters, 2010,31(11):1377-1387
45 Zhang Zutao, Zhang, Jiashu. A New Real-time Eye Tracking Based on Nonlinear Unscented Kalman Filter for Monitoring Driver Fatigue. Journal of Control Theory and Applications, 2010,8(2):181-188
46 Zhang Tao, Cai Canhui. A Face Tracking Algorithm Based on Multiple Feature Mean Shift. Journal of Electronics and Information Technology, 2009,31(8):1816-1820
47 Li Shuxiao, Chang Hongxing, Zhu Chengfei. Adaptive Pyramid Mean Shift for Global Real-time Visual Tracking. Image and Vision Computing, 2010,28(3):424-437
48 Ido Leichter, Michael Lindenbaum, Ehud Rivlin. Mean Shift Tracking with Multiple Reference Color Histograms. Computer Vision and Image Understanding, 2010,114(3): 400-408
49王新红,王晶,田敏.基于空间边缘方向直方图的Mean Shift跟踪算法.中国图象图形学报,2008,13(3):586-592
50朱胜利,朱善安.核函数带宽自适应的Mean Shift目标跟踪算法.光电工程,2006, 33(8):11-16
51张恒,李立春,于起峰.尺度方向自适应Mean Shift跟踪算法.光学精密工程,2008, 16(6):1133-1139
52左军毅,梁彦,赵春晖,等.一种新的基于Mean Shift的目标三自由度跟踪算法.电子与信息学报,2008,30(1):172-175
53 Marko Heikkila, Matti Pietikainen. A Texture-based Method for Modeling the Background and Detecting Moving Objects. IEEE Trans on Pattern Analysis and Machine Intelligence, 2006,28(4):657-662
54郑永斌,黄新生,丰松江.SIFT和旋转不变LBP相结合的图像匹配算法.计算机辅助设计与图形学学报,2010,22(2):286-292
55应自炉,蔡淋波,刘召义.基于LBP的拉普拉斯特征映射人脸识别.信号处理,2010,26(8):1230-1233
56 Ning Jifeng, Wu Chengke. Robust Object Tracking Using Joint Color-texture Histogram. Intenationgal Journal of Pattern Recognition and Artificial Intelligence , 2009,23(7):1245-1263
57 F. Friedrichs.Camera-based Drowsiness Reference for Driver State Classification under Real Driving Conditions. IEEE International Conference on Intelligent Vehicles Symposium, La Jolla, CA, United states, 2010:101-106
58朱振华,吴晓娟,王磊,等.基于眨眼持续时间的司机疲劳检测方法.计算机工程,2008:34(5):201-206
59 P. Smith, M. Shah, N. V. Lobo. Determining Driver Visual Attention with One Camera. IEEE Trans on Intelligent Transportation Systems, 2003,4(4):205-218
60 Shanshan Zhang, Fuqiang Liu, Zhipeng Li. An Effective Driver Fatigue Monitoring System. IEEE International Conference on Machine Vision and Human-machine Interface, Kaifeng, China, 2010: 279-282
61 M. Bergasa Luis, Jesus Nuevo, Miguel A. Sotelo, Rafael Barea, et al. Real-time System for Monitoring Driver Vigilance. IEEE Trans on Intelligent Transportation Systems, 2006,7(1):63-77
62 Fan Xiao, Yin Baocai, Sun Yanfeng. Yawning Detection for Monitoring Driver Fatigue. Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, China, 2007,2:664-668
63 Li Lingling, Chen Yangzhou, Li Zhenglong. Yawning Detection for Monitoring Driver Fatigue Based on Two Cameras. IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, United states, 2009:12-17
64 Azim Tayyaba, M. Arfan. Jaffar, Mirza Anwar Majid. Automatic Fatigue Detection of Drivers Through Pupil Detection and Yawning Analysis. 4th International Conference on Innovative Computing, Information and Control, Kaohsiung, Taiwan, 2009:441-445
65 Azim Tayyaba, M. Arfan. Jaffar, Mirza Anwar Majid. Real Time Fatigue Detection of Drivers Through Eye Closure Duration And Yawning Analysis. ICIC Express Letters, 2010,4(3):725-731
66 Chu Jiangwei, Jin Lisheng, Tong Binliang, et al. A Monitoring Method of Driver Mouth Behavior Based on Machine Vision. IEEE International Conference on Intelligent Vehicles Symposiuim, Parma, Italy, 2004:351-356
67 F. Torre Dela. Subspace Eyetracking for Driver Warning. IEEE International Conference on Image Processing, Barcelona, Spain, 2003,3:329-332
68 A. Caldwell John, J. Lynn Caldwell, M. Schmidt Regina. Alertness Management Strategies for Operational Contexts. Sleep Medicine Reviews, 2008,12(4):257-273
69 A. V. Desai, M. A. Haque. Vigilance monitoring for Operator Safety: A Simulation Study on Highway Driving. Journal of Safety Research, 2006,37(2):139-147