【精品文档】25中英文双语外文文献翻译成品：基于人脸识别技术的驾驶员疲劳监测系统

此文档是毕业设计外文翻译成品（ 含英文原文+中文翻译），无需调整复杂的格式！下载之后直接可用，方便快捷！本文价格不贵,也就几十块钱！一辈子也就一次的事！外文标题：The Driver Fatigue Monitoring System Based on Face Recognition Technology外文作者：Xiao-qing Luo , Rong Hu, Tian-e Fan文献出处:International Conference on Intelligent Control & Information Processing,2018(如觉得年份太老，可改为近2年，毕竟很多毕业生都这样做)英文2486单词， 12908字符(字符就是印刷符)，中文3769汉字。原文：The Driver Fatigue Monitoring System Based on Face Recognition TechnologyXiao-qing Luo , Rong Hu, Tian-e FanAbstractThis paper uses different algorithms, which are called AdaBoost algorithm and the difference between infrared frames algorithm, to locate the precise position of the eyes in different light environment of driving. We identify the eyes status by extracting the characteristic parameters of eyes and detect fatigue based on the method of PERCLOS. At the same time, tfurther test the drivers fatigue, we use the Local Binary Patter (LBP) algorithm to detect the yawning as an aided detection. The results of the experiment show that algorithm ensures the accuracy of the system and it can achieve the requirement of non contact type, different lighting conditions and real-time detection.Keywords:Driver fatigue; Yawning detection; Eye detection; PERCLOS; AdaBoost.IntroductionNowadays, fatigue driving is one of the main reasons for traffic accidents 1. Its reported that there were 3906164 traffic accidents in 2010 in our country. Among them, 92% of accident deaths were caused by motor vehicle driver speeding, while most of accident deaths caused by the fatigue driving roses 1% 2. At present, there are already some methods of fatigue testing such as testing through the position of head, EEG, EKG, eye blinking, PERCLOS and so on 3. And as a non-contact method, PERCLOS method is able to reflect peoples situation of eyelids and their degree of fatigue when they are sleepy, but its easy to be influenced by the lighting. This paper analyzes the brightness of picture which is taken by CCD camera to estimate day or night. We utilize AdaBoost algorithm to locate peoples faces and eyes in the daytime, while at night we use infrared frame difference to test their eyes 4. We judge drivers condition of eyes through extracting characteristic parameters of the eyes, including the ratio of high and wide and eyelid curvature. And then adopt the method of PERCLOS to test drivers fatigued degree. In addition, after locating drivers eyes, we locate drivers mouth according to their characters of faces. Then use LBP algorithm to test yawn and judge drivers fatigued state. The study on fatigued driving system has a significance on the prevention of traffic accidents 5.System frameworkThe framework of driver fatigue warning system can be seen in Figure 1, including the infrared light source CCD sensor camera which can control the near-far, the image pre- processing module, module of fatigue detection and alarm devices 6. CCD camera is being introduced to pick up drivers face image. And the image pre-processing module is used to obtain the image histogram equalization, smoothing pre-processing operation. Fatigue detection module is to calculate eyes closed extent by extracting characteristic parameters of eyes and measure the P80 (the eye status is determined to close when the closure degree is greater than 80 percent) which was got by using PERCLOS method to detect drivers fatigue. Alarm device is used to determine driver fatigue, if driver is tired, an alarm signal will be found 7.Image processingThe processing system is shown in Figure 2. The system estimates the image daytime or night according to the brightness of it. When the brightness is greater than a certain threshold (45), it is the daytime image and daytime mode algorithm is used for image processing, to the contrary, the processing algorithm using the night mode.Image pre-processingThe adaptability of the algorithm and the accuracy of detection will be affected by many factors if direct detection due to the collected images is influenced by the illumination, background, noise and other factors. Therefore, we have to pre-process these images. In order to efficiently distinguish between foreground and background, in the stage of image pre-processing, lighting compensation and image histogram equalization of treatment are done first to enhance the contrast of image. Meanwhile, to eliminate the influence of noise, the operation of image median filtering can not only removes isolated point noise, but also effectively protects the image boundary information; In addition, the controllable distance infrared CCD camera is applied to capture images under low light conditions 7.Process in day modeEyes location and face detectionThis paper proposes a face detection method named AdaBoost to detect eyes and face with rectangular characteristics. This method is composed of integral image, AdaBoost algorithm and cascade detector 8. Also it can solve the complexity of face and eye detection problem well. At the same time, the AdaBoost is experimentally proved good real- time and high efficient detection. According to the way of weighted voting, the AdaBoost can cascade the weak classifier level to a strong classifier. The definite means are as follows:a) Input the training sample data N : (x1, y1), (x2 , y2 ), , (xn , yn ) ;b)Initializing the weightsand l denotes the number of positive and negative samples respectively;c)For t =1 to T , first normalize weights, For hj of each feature j , Calculating the error rate relative to the current weightsthen choose the weak classifier with the smallest error rate as number t . Finally, update the corresponding weight of each sample,d)Output the last strong classifier:which 0 denotes negative data and 1 denotes positive data.e)At the end, composing all the strong classifier into a cascade detector.Figures 3 and 4 show the test effect pictures in the day mode. As some parts in face have characteristics of rectangular, which similar to the eyes, such as eyebrows, nostrils, mouth and other parts, leading to the high risk of error detection. In this paper, we make use of the geometric features of eyes to further choose rectangle and locate the eyes precisely.Extraction eyes characteristic parametersAfter extracting eyes contour point, then we need to calculate the eyes characteristic parameters. On the basis of analyzing eyes characteristics of state recognition,calculating the characteristic parameters of eyes such as aspect ratio and eyelid curvature.a)Eyes height to width ratio(EyeHWRate of eye)During the eye feature extraction, as shown in Figure 5, measuring the height of eyes (EyeHeight) and width (EyeWidth) to calculate the height to width ratio of the eyes. The EyeHWRate is showed as follow:b)Upper eyelid curvatureIt can be seen from the picture of the edges of eyelids, in the process of opening or closing your eyes, the upper eyelid curvature changed a lot, while lower eyelid curvature basically remain unchanged. This is the reason why we choose upper eyelid curvature as feature parameter. In the process of treatment the eyes, the errors of the pixel in the left and right side of eyes will be happened, so we need to indent five pixels for eyelid.As shown in Figure 6, from A to C, from B to D. We pick up intermediate portion in edge curve of eyes because there is more accurate of eye states reflected by the curvature of eyelid located in, and E is the middle on the eyes of the extracted contour points. We find the first white pixels from the leftmost and rightmost, and then take the pixel values corresponding to the midpoint between the two, if we got 0, it is judged as a recess and on the contrary, it is a convex.Fatigue judgmentsAs the analysis above, the eye can be regarded as being closed when EyeHWRate 270 . Also, when the EyeHWRate is between 270 and 400, then decide whether it is open by curvature. If the judge is concave and EyeHWRate 400 , it is open.Write down the number of eyes open and close, as well as the time it begins, when it ends. We can calculate the value of PERCLOS to judge the drivers fatigue. If the system ascertains the driver in fatigue state, then voice warning.Process in night modeIf the images collected from the case at night dark, human eyes will be influenced by light red-eye. So we use the infrared inter-frame difference algorithm to locate the eye. Around the CCD camera, an controlled infrared light source was installed between adaxial and abaxial, adaxial light produce the bright pupil when the odd frame was controlled by the program, just as shown in Figure 7 (a). In the sameway, abaxial light produce the bright pupil when the even frame was controlled, it is shown in Figure 7 (b). Considering there is smallest variations among the parity frame image, only pupils vary greatly, so the video sequence of eyes can be determined by using Frame-difference method.In image processing, the opening and closing operation of binary mathematical morphology is usually used to process segmented image 9, after opening operation, we use the method of image processing including dilation, erosion to remove isolated points and burrs. At end, to calculate the eye area that is occupied. In a lab environment, setting the area threshold value of 50, when the area is less than 50, eyes are closed, otherwise, eyes are open. Figure 8 show the night mode with eyes closed treatment effect diagram.Auxiliary warning processingA large extent of the fatigue is reacted in the eye, but not all. We can also watch the reactions in the mouth. In this paper, LBP algorithm is presented to detect yawn. The LBP algorithm is as follows:Among them, gc represent all pixels gray values in local regional center, g0 to g p -1represent neighbor pixels gray regional center. The arc centered at gc whose radius is the R. The operator has a gray scale invariance, With the only illumination changes, which will not change the order of local pixel values. The same to the value obtained by the operator.In Case of eye is located, we need to detect the production of the yawn 10, first we can locate the position of mouth area according to face organ distribution. Horizontal distance of eyes center is approximately equal to the vertical distance of eyes to mouth; while the width of the mouth is roughly equivalent to a horizontal distance of eyes center. Thus, according to these proportions, we determine roughly the mouth region, and then reuse the LBP algorithm for further testing of the yawn. Empirical analysis is based on multiple analog mouth yawning open state, when open state is over 70, we regard this is the first yawn. So we can determine the extent of fatigue according to the length and frequency of yawning. Figure 9 show the yawn detection renderings.Experimental resultIn order to verify the effect of fatigue driving test, we simulate monitoring for the driver in fatigue state. Then to detect the laboratory personnel respectively in different lighting conditions (morning, afternoon, evening) as well as in different states (awake and fatigue). The accuracy of discriminant results is over 90%. Specific test results are shown in table 1.ConclusionThis paper introduces a method of driver fatigue warning,which gots a strong robustness classifier based on the training of AdaBoost algorithm to locate the eyes, and to judge the state of the eyes through the calculation of height-width ratio and eyelid curvature. While, in the night mode, using the method of infrared frame difference can locate the eyes accurately, then determine the fatigue level by the value of PERCLOS. Moreover, by testing the yawn based on LBP algorithm, we could further judge the degree of fatigue. The system this paper proposed has the advantages of low cost of computation and high accuracy, being practical , non-contact, sensitivity and reliability. It can improve the effectiveness and accuracy of the fatigue detection.References1M. Jia, et al., Research on drivers face detection and position method based on image processing, in 2012 24th Chinese Control and Decision Conference, CCDC 2012, May 23, 2012 - May 25, 2012, Taiyuan, China, 2012, pp. 1954-1959.2SONG Zhumei, WANG Hailin, LIU Hanhui. Drive Fatigue Monitoring and Identification Methods J. Journal of Shenzhen Institute of Information Technology, 2011, pp. 38-42.3Q. Ji, et al., Real-time nonintrusive monitoring and prediction of driver fatigue, IEEE Transactions on Vehicular Technology, vol. 53, pp. 1052-1068, 2004.4Q. Yangon, et al., A novel real-time face tracking algorithm for detection of driver fatigue, in 2010 International Symposium on Intelligent Information Technology and Security Informatics, IITSI 2010, April 2, 2010 - April 4, 2010, China, 2010, pp. 671-676.5L. M. Bergasa and J. Nuevo, Real-time system for monitoring driver vigilance, in IEEE International Symposium on Industrial Electronics 2005, ISIE 2005, June 20, 2005 - June 23, 2005, Dubrovnik, Croatia, 2005, pp. 1303-1308.6P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, in 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, December 8, 2001 - December 14, 2001, Kauai, HI, United states, 2001, pp. I511-I518.7P. Campadelli, et al., Precise eye and mouth localization, International Journal of Pattern Recognition and Artificial Intelligence, vol. 23, pp. 359-377, 2009.8J. Wu, et al., Fast asymmetric learning for cascade face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 369-382, 2008.9T. Ahonen, et al., Face description with local binary patterns: Application to face recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 2037-2041, 2006.10X. Fan, et al., Yawning detection for monitoring driver fatigue, in 6th International Conference on Machine Learning and Cybernetics, ICMLC 2007, August 19, 2007 - August 22, 2007, Hong Kong, China, 2007, pp. 664-668.11M.Balasubtamanian, S.Palanivel, V. Ramalingam, Real time face and mouth recognition using radial basis function neural networks. Expert System with Applications (2008),doi:10.10.1016/j.eswa.2008.08.001.12P.S. Rau. Drowsy driver detection and warning system for commercial vehicle drivers: field operational test design, data analyses, and progress. Proceedings of the 19th International Conference on Enhanced Safety of Vehicles, Washington, DC, June 6-9, 2005.13S. Park, M. Trivedi. Driver activity analysis for intelligent vehicles: issues and development framework. Proceedings of IEEE Intelligent Vehicles Symposium, Las Vegas, USA, June 2005, pp. 795-800.14G. Yang, Y. Lin, P. Bhattacharya. A driver fatigue recognition model using fusion of multiple features. Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Hawaii USA, vol. 2, October 2005, pp. 1777-1784.15Z. Zhu, Q. Ji, P. Lan. Real time non intrusive monitoring and prediction of driver fatigue. IEEE Transactions on Vehicular Technologies, 53 (4) (2004), pp. 1052-1068.译文：基于人脸识别技术的驾驶员疲劳监测系统Xiao-qing Luo , Rong Hu, Tian-e Fan摘要在本文中，利用不同的AdaBoost算法和红外图像序列算法之间的差异，来定位在不同光照驾驶环境下眼睛的精确位置。 我们通过提取眼睛的特征参数来识别眼睛的状态，并且在PERCLOS方法的基础上去监测疲劳驾驶。同时，为了进一步监测驾驶员的疲劳程度，我们使用局部二元模式（LBP）算法来监测驾驶员打呵欠的行为以作辅助检测。 实验结果表明该算法保证了系统的准确性，达到了非接触式、不同光照条件和实时监测的要求。关键词：疲劳驾驶，打哈欠监测，眼睛监测，PERCLOS方法， AdaBoost算法.引言疲劳驾驶是目前交通事故发生的主要原因之一1。据悉，2010年我国共发生交通事故3906164起。其中，92的事故死亡是由机动车驾驶员超速驾驶造成的，而大部分事故死亡则是由疲劳驾驶引起的并提升了12。目前已经有一些疲劳驾驶的监测方法，如通过头部位置、EEG、EKG、眨眼、PERCLOS等进行监测3。作为一种非接触式方法，PERCLOS方法能够监测出人们在困倦时眼睑的状况和疲劳程度，但是它很容易受到光照的影响。本文分析了CCD摄像机拍摄图像的亮度去评估白天或夜间的环境。我们利用AdaBoost算法在日间来定位人脸和眼睛，而在夜间我们使用红外图像序列差异来监测他们的眼睛4。我们通过提取眼睛的特征参数来判断驾驶员眼睛的状况，包括高宽比和眼睑曲率。然后采用PERCLOS方法测试驾驶员的疲劳程度。另外，在定位驾驶员的眼睛之后，我们根据他们的面部特征定位驾驶员的嘴巴。然后使用LBP算法测试打哈欠的行为并判断驾驶员的疲劳状态。对疲劳驾驶系统的研究对预防交通事故具有重要意义5。系统架构从图1可以看到驾驶员疲劳驾驶预警系统的框架，包括可以控制近场红外光源CCD传感器摄像头，图像预处理模块，疲劳检测模块和报警装置6。 还介绍了CCD摄像头来监测驾驶员的脸部图像。 图像预处理模块用于获取图像直方图均衡、平滑预处理操作。 疲劳检测模块通过提取眼睛的特征参数来计算眼睛的闭合程度，并利用PERCLOS方法检测驾驶员的疲劳程度并测量P80（闭合度大于80时判断为眼睛闭合状态）。 报警装置用于确定驾驶员是否疲劳驾驶，如果驾驶员感到疲劳，则会发现报警信号7。 红外光源CCD相机 图像处理模块 疲劳监测模块 报警装置 控制装置 图一 系统框架图像处理图像处理系统如图2所示。系统根据图像在白天或夜间的亮度去评估图像。 当亮度大于某个阈值（45）时，采用白天图像和白天模式算法进行图像处理，相反，则采用夜间模式的处理算法。 图二 算法流程图图像预处理当直接检测采集的图像受到光照、背景、噪声和其他因素的影响时，则该算法的适用性和检测的准确性也将受到许多因素的影响。 因此，我们必须预先处理这些图像。为了有效区分前景和背景，在图像预处理阶段首先进行光照补偿和图像直方图均衡进行处理，以提高图像的对比度。 同时，为了消除噪声的影响，图像中值滤波的运算不仅消除了孤立点的噪声，而且有效地保护了图像的边界信息; 此外，可控距离红外CCD摄像机可用于在低光照条件下拍摄图像7。日间模式下的处理眼睛位置和面部检测本文提出了一种名为AdaBoost的人脸检测方法来检测具有矩形特征的眼睛和脸部。 该方法由积分图像、AdaBoost算法和级联检测器组成8。 也可以很好地解决人脸检测问题的复杂性。 与此同时，AdaBoost通过实验证明其具有良好的实时性和高效性。 根据不同加权方式，AdaBoost可以将弱分类器级别联到强分类器。 方式如下：b) 输入样本数据N：(x1, y1), (x2 , y2 ), , (xn , yn ) ;b)初始化权参数1和0分别表示正样本和负样本的数量。c) t 的取值范围从1 到T , 首先对权参数正态化, j的每个特性 hj , 计算当前权参数的错误率然后选择弱分类器 以数字t表示最小的错误率。 最后，更新每个样本的相应权参数，,d)输出最后一个强分类器：其中0表示负数据，1表示正数据。e）最后，将所有强分类器组成一个级联检测器。图3和4显示了日间模式下的测试效果图。 由于脸上的某些部位具有长方形的特征，类似于眼睛，例如眉毛、鼻孔、嘴巴等部位，这容易导致错误检测的高风险。 在本文中，我们利用眼睛的几何特征进一步选择矩形并精确定位眼睛。图三 日间模式下眼部睁开处理图四 夜间模式下眼部闭合处理提取眼睛特征参数提取眼睛轮廓点后，我们需要计算眼睛特征参数。在分析眼睛状态识别特征的基础上，计算眼睛的长宽比和眼睑曲率等特征参数。a）眼睛高宽比（EyeHWRate） 图五 眼睛高宽比在眼部特征提取过程中，如图5所示，测量眼睛的高度（EyeHeight）和宽度（EyeWidth）来计算眼睛的高宽比。 眼睛高宽比计算公式如下：b）上眼睑曲率从眼睑边缘的图片可以看出，在打开或闭上眼睛的过程中，上眼睑的曲率变化很大，而下眼睑的曲率基本保持不变。 这就是我们选择上睑曲率作为特征参数的原因。 在处理眼睛的过程中，会出现眼睛左右两侧像素的误差，因此我们需要缩小眼睑的五个像素。如图6所示，从A到C，从B到D.我们截取眼睛边缘曲线中的中间部分，因为眼睛的状态通过由位于其中的眼睑曲率能得到更准确的反应，E是眼睑提取轮廓点的中心点。 我们从最左边和最右边找到第一个白色像素，然后取对