中文2960字
译文一
基于PAC的实时人脸检测和跟踪方法
摘要：
这篇文章提出了复杂背景条件下，实现实时人脸检测和跟踪的一种方法。这种方法是以主要成分分析技术为基础的。为了实现人脸的检测，首先，我们要用一个肤色模型和一些动作信息(如：姿势、手势、眼色)。然后，使用PAC技术检测这些被检验的区域，从而判定人脸真正的位置。而人脸跟踪基于欧几里德（Euclidian）距离的，其中欧几里德距离在位于以前被跟踪的人脸和最近被检测的人脸之间的特征空间中。用于人脸跟踪的摄像控制器以这样的方法工作：利用平衡/（pan/tilt）平台，把被检测的人脸区域控制在屏幕的中央。这个方法还可以扩展到其他的系统中去，例如电信会议、入侵者检查系统等等。
视频信号处理有许多应用，例如鉴于通讯可视化的电信会议，为残疾人服务的唇读系统。在上面提到的许多系统中，人脸的检测喝跟踪视必不可缺的组成部分。在本文中，涉及到一些实时的人脸区域跟踪[1-3]。一般来说，根据跟踪角度的不同，可以把跟踪方法分为两类。有一部分人把人脸跟踪分为基于识别的跟踪喝基于动作的跟踪，而其他一部分人则把人脸跟踪分为基于边缘的跟踪和基于区域的跟踪[4]。
基于识别的跟踪是真正地以对象识别技术为基础的，而跟踪系统的性能是受到识别方法的效率的限制。基于动作的跟踪是依赖于动作检测技术，且该技术可以被分成视频流（optical flow）的（检测）方法和动作—能量（motion－energy）的（检测）方法。
基于边缘的（跟踪）方法用于跟踪一幅图像序列的边缘，而这些边缘通常是主要对象的边界线。然而，因为被跟踪的对象必须在色彩和光照条件下显示出明显的边缘变化，所以这些方法会遭遇到彩色和光照的变化。此外，当一幅图像的背景有很明显的边缘时，（跟踪方法）很难提供可靠的（跟踪）结果。当前很多的文献都涉及到的这类方法时源于Kass et al.在蛇形汇率波动[5]的成就。因为视频情景是从包含了多种多样噪音的实时摄像机中获得的，因此许多系统很难得到可靠的人脸跟踪结果。许多最新的人脸跟踪的研究都遇到了最在背景噪音的问题，且研究都倾向于跟踪未经证实的人脸，例如臂和手。
在本文中，我们提出了一种基于PCA的实时人脸检测和跟踪方法，该方法是利用一个如图1所示的活动摄像机来检测和识别人脸的。这种方法由两大步骤构
成：人脸检测和人脸跟踪。利用两副连续的帧，首先检验人脸的候选区域，并利用PCA技术来判定真正的人脸区域。然后，利用特征技术（eigen－technique）
跟踪被证实的人脸。
在这一部分中，将介绍本文提及到的方法中的用于检测人脸的技术。为了改进人脸检测的精确性，我们把诸如肤色模型[1,6]和PCA[7,8]这些已经发表的技术结合起来。
检测肤色像素提供了一种检测和跟踪人脸的可靠方法。因为通过许多视频摄像机得到的一幅RGB图像不仅包含色彩还包含亮度，所以这个色彩空间不是检测肤色像素[1,6]的最佳色彩图像。通过亮度区分一个彩色像素的三个成分，可以移动亮度。人脸的色彩分布是在一个小的彩色的色彩空间中成群的，且可以通过一个2维的高斯分部来近似。因此，通过一个2维高斯模型可以近似这个肤色模型，其中平均值和变化如下：
m=(,
) 其中
＝
，
＝
（1）
＝
（2）
一旦建好了肤色模型，一个定位人脸的简单方法是匹配输入图像来寻找图像中人脸的色彩群。原始图像的每一个像素被转变为彩色的色彩空间，然后与该肤色模型的分布比较。
虽然肤色在特征的应用种非常广泛，但是当肤色同时出现在背景区域和人的皮肤区域时，肤色就不适合于人脸检测了。利用动作信息可以有效地去除这个缺点。为了精确，在肤色分类后，仅考虑包含动作的肤色区域。结果，结合肤色模型的动作信息导出了一幅包含情景（人脸区域）和背景（非人脸区域）的二进制图像。这幅二进制图像定义为 ，其中It(x,y)
和It-1(x,y)分别是当前帧和前面那帧中像素（x,y）的亮度。St是当前帧中肤色像素的集合，（斯坦）t是利用适当的阈限技术计算出的阈限值[9]。作为一个加速处理的过程，我们利用形态学（上）的操作（morpholoical operations）和连接成分分析，简化了图像Mt。
2.3利用PCA检验人脸
因为有许多移动的对象，所以按序跟踪人脸的主要部分是很困难的。此外，还需要检验这个移动的对象是人脸还是非人脸。我们使用特征空间中候选区域的分量向量来为人脸检验问题服务。为了减少该特征空间的维度，我们把N维的候选人脸图像投影到较低维度的特征空间，我们称之为特征空间或人脸空间[7,8]。在特征空间中，每个特征说明了人脸图像中不同的变化。
为了简述这个特征空间，假设一个图像集合I1，I2，I3，…，IM，其中每幅图像是一个N维的列向量，并以此构成人脸空间。这个训练（测试）集的平均值用A＝来定义。用
i＝I I－A来计算每一维的零平均数，并以此构成一个新的向量。为了计算M的直交向量，其中该向量是用来最佳地描述人脸图像地分布，首先，使用C＝
i
ir＝YYr （4）来计算协方差矩阵Y＝[
1
2…
M]。虽然矩阵C是N×N维的，但是定义一个N维的特征向量和N个特征值是个难处理的问题。因此，为了计算的可行性，与其为C找出特征向量，不如我们计算[YTY]中M个特征向量vk和特征值
k，所以用u k＝
来计算一个基本集合，其中k＝1，…，M。关于这M个特征向量，选定M个重要的特征向量当作它们的相应的最大特征值。对于M个训练（测试）人脸图像，特征向量W i＝[w 1，w 2，…，w M’]用w k＝u kT
i,k=1,…，M（6）来计算。
为了检验候选的人脸区域是否是真正的人脸图像，也会利用公式（6）把这个候选人脸区域投影到训练（测试）特征空间中。投影区域的检验是利用人脸类和非人脸类的检测区域内的最小距离，通过公式（7）来实现的。Min（||Wkcandidate－Wface||,||Wkcandidate－Wnonface||），（7）其中Wkcandidate是训练（测试）特征空间中对k个候选人脸区域，且Wface，Wnonface分别是训练(测试)特征空间中人脸类和非人脸类的中心坐标，而||×||表示特征空间中的欧几里德距离（Euclidean）
在最新的人脸检测中，通过在特征空间中使用一个距离度量标准来定义图像序列中下一幅图像中被跟踪的人脸。为了跟踪人脸，位于被跟踪人脸的特征向量和K个最近被检测的人脸之间的欧几里德距离是用obj＝argkmin||Wold－Wk||，k＝1，…，K，（8）来计算的。
在定义了人脸区域后，位于被检测人脸区域的中心和屏幕中心之间的距离用distt（face，screen）＝Facet（x，y）－Screen（height/2，width/2），（9）来计算，其中Facet（x，y）是时间t内被检测人脸区域的中心，Screen（height/2，width/2）是屏幕的中心区域。使用这个距离向量，就能控制摄像机中定位和平衡/倾斜的持续时间。摄像机控制器是在这样的方式下工作的：通过控制活动摄像机的平和/倾斜平台把被检测的人脸区域保持在屏幕的中央。在表2自己品母国。参数表示的是活动摄像机的控制。用伪代码来表示平衡/倾斜处理的持续时间和摄像机的定位。
计算平和/倾斜持续时间和定位的伪代码：
Procedure Duration（x，y）
Begin
Sigd=None；
Distance=；
IF distance> then
Sigd=Close；
ELSEIF distance> then
Sigd=fat；
Return（Sigd）；
End Duration；
Procedure Orientation（x，y）
Begin
Sigo=None；
IF x> then
Add “RIGHT” to Sigo；
ELSEIF x<- then
Add “LEFT” to Sigo；
IF y> then
Add “up”to Sigo；
ElSEIF x<- then
Add “DOWN” to Sigo；
Return（Sigo）；
End Orientation；
本文中提议了一种基于PAC的实时人脸检测和跟踪方法。被提议的这种方法是实时进行的，且执行的过程分为两大部分：人脸识别和人脸跟踪。在一个视频输入流中，首先，我们利用注入色彩、动作信息和PCA这类提示来检测人脸区域，然后，用这样的方式跟踪人脸：即通过一个安装了平衡/请求平台的活动摄像机把被检测的人脸区域保持在屏幕的中央。未来的工作是我们将进一步发展这种方法，通过从被检测的人脸区域种萃取脸部特征来为脸部活动系统服务。
[1] Z. Guo, H. Liu, Q. Wang, and J. Yang, “A Fast Algorithm of Face Detection for Driver Monitoring,” In Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, vol.2, pp.267 - 271, 2001.
[2] M. Yang, N. Ahuja, “Face Detection and Gesture Recognition for Human-Computer Interaction,” The International Series in Video Computing , vol.1, Springer, 2001.
[3] Y. Freund and R. E. Schapire, “A Decision-Theoretic Generaliztion of On-Line Learning and an Application to Boosting,” Journal of Computer and System Sciences, no. 55, pp. 119-139, 1997.
[4] J. I. Woodfill, G. Gordon, R. Buck, “Tyzx DeepSea High Speed Stereo Vision System,” In Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop, pp.41-45, 2004.
[5] Xilinx Inc., “Virtex-4 Data Sheets: Virtex-4 Family Overview,” Sep. 2008. DOI= http://www.xilinx.com/
[6] Y. Wei, X. Bing, and C. Chareonsak, “FPGA implementation of AdaBoost algorithm for detection of face biometrics,” In Proceedings of IEEE International Workshop Biomedical Circuits and Systems, page S1, 2004.
[7] M. Yang, Y. Wu, J. Crenshaw, B. Augustine, and R. Mareachen, “Face detection for automatic exposure control in handheld camera,” In Proceedings of IEEE international Conference on Computer Vision System, pp.17, 206.
[8] V. Nair, P. Laprise, and J. Clark, “An FPGA-based people detection system,” EURASIP Journal of Applied Signal Processing, 2005(7), pp. 1047-1061, 2005
[9] C. Gao and S. Lu, “Novel FPGA based Haar classifier face detection algorithm acceleration,” In Proceedings of International Conference on Field Programmable Logic and Applications, 2008.
外文原文一
PCA-Base Real-Time Face Detection and Tracking1
【Abstract】：
This article put forward complicated background term next; realize solid contemporaries face examination with on the trail of a kind of method. These kinds of method regard main composition analysis technique as basal. Facial examination in person for realizing, first, we want to use a skin color model to act the information with the some (such as: Posture, signal, expression of eyes).Then, the usage PAC technique examines these drive the district that examine, from but judge a real position. But person's face follows according to the is several in the virtuous (Euclidian) distance of, among them the is several to reign in the virtuous distance in past drive on the trail of person's face with recent drive the person who examine the characteristic space inside of the a. Useding for a for following resembles the controller the work in such way: Make use of equilibrium/ tilt to one side (pan/ tilt) the terrace, examine drive of person a district controls at hold the act central. This method cans also expand to go to in the other system, for example telecommunication meeting, invader check system etc.
1 preface
Seeing the signal of handles many applications, for example owing to the communication can see the telecommunication meeting that turn, for disable and sick person service of the lips reads the system. In up many systems that mention, the facial examination in person drink to follow to see to can't lack necessarily of constitute the part. In this text, involve the some solid of person a district follows the [1 － 3 ] .By any large, according to follow the angle different, can is divided in to follow the method two types. Reach a the part of people follows person's face is divided into according to identify on the trail of to drink according to act of on the trail of, but other a the part of people then follows person's face is divided into according to edge of on the trail of with on the trail of [that according to district 4].
According to the on the trail of that identify is really with the object identifies technique is basal, but follow the function of the system is the restrict of the efficiency to suffer to identify the method. According to the on the trail of of the action is a method to depend on to examine the technique in the action, and that technique can be been divided in to see flow( optical flow) with the method that act the — energy( motion － energy).
According to the method of the edge useds for the edge that follow a picture
preface row, but these edgeses is usually the boundary line of the main object.However, because were musted shine on with the light at the color by the on the trail of object the term descends to display the obvious edge changes, so these methodses will fall among the color with the variety that light shine on.In addition, be a background of picture contain very obvious edge,( follow the method) dependable result in very difficult offering.Current this type of method that a lot of cultural heritages all involve come from the Kass et al.In the snake form rate of exchange motion [ 5 the achievement of ]s.Because see the scene of to acquire from included various the noise of varieties solid the hour the resemble the machine of, therefore many systems is very rare to dependable person's face to follow the result.Many latest a research for followings met most problem in background noise, and the research inclines toward person's face that follow has not yet the proof, for example arm with hand.
In this text, we put forward a kind of according to PCA solid contemporaries an examination with follow the method, that method is an activity to make use of a,such as figure,1 show resemble machine to examine with identify the person facial.This kind of method from two greatest steps composing:Person an examination with person's face follow.Make use of two continuouses, examine a person's face candidate for election districts first, combine exploitation PCA technique to judge the real person a district.Then, make use of the characteristic technique( eigen － technique) follow to confirmed person's face.
2 Person an examination
In this first part, will introduce the method that this text mention inside of used for the technique that examine person's face.For improves an accurate for examining, we announce such as the skin color model [ 1,6 ] with PCA [ 7,8 ] these already of the technique knot puts together.
2.1 skin color classification
The examination skin color pixel provides a kind of examination with follow the facial and dependable method in person.Because pass many that sees the machine resemble a RGB picture not only include color but also gets bright degree in containment, so this color space is not the best color to examine the skin color pixel [ 1,6 ] picture.Pass bright a three compositions for distinguishing analyse a color pixel, can move bright degree.A Gauss for of color distributing is in a small chromatic color space large groupsly, and can passing first 2 cent department to look like.Therefore, pass a 2 Gauss models can look like this skin color model, among them average value with change as follows:
m=(,
) 其中
＝
，
＝
（1）
＝
（2）
Once set up to like the skin color model, a positions facial and simple method in person is match the importation picture to look for facial color in middleman in picture cluster.Each a pixel of the primitive picture were changed into the chromatic color space, then distributing with the skin color's model the comparison.
2.2 action examination
Although the skin color application in characteristic grows very extensive, when the skin color appear at the same time in the background district with the person's skin district, skin color is not suitable for in the person an examination.Making use of to act information can away with this weakness availably.For the sake of the precision, after the skin color divides into section, consider the skin color district of the containment action only.Result, the action information of the combination skin color model leads a binary system for a containment scene( person's a district) with background( not person's a district) picture.This binary system picture definition is, among them It( x, y)
With the It-1( x, y) respectively is a bright degree for with front an inside pixel( x, y).The St is a current an inside skin color pixel to gather, the t is a worth [ in limit in to makes use of appropriate limit technique compute 9 ] .The acceleration that be used as a process handles, we make use of the operation( morpholoical operations) that appearance learn( top) with link the composition analyzes, simplifying the picture Mt.
2.3 make use of the PCA examine person's face
There is many ambulatory objects, so follow in sequence the facial and main part in person is very difficult.In addition, return the demand examine this ambulatory object is person's face or not person's face.We uses characteristic space inside the weight vector of the candidate for election district to behave face examination problem service.For reducing that characteristic the spatial a candidate for, we N a picture casts shadow the characteristic space of the lower the degree of , we call it as characteristic space or persons a space [ 7,8 ] .In characteristic space, each characteristic explained the different variety in inside in a picture in person.
Say this characteristic space for the sake of Chien, suppose a picture gather the I1, I2, I3, … , IM, among them each picture is the row vector of a N , and with this composing person a space.The average value that this training( test) gather uses the A= define.Use the i= the I I － A computes the zero average number of each , and with this composing a new vector.For computing the M keep handing over vector, among them that vector is to uses to come to describe the person best a picture ground distribute, first, the usage C= i ir= YYr(4) compute to help the square and bad matrix Y the =[1 2 … Ms].Although matrix C is characteristic vector that N × N of, define a N is a difficult processed problem with the N a characteristic value.Therefore, for the sake of calculating possibility, with its finds out the characteristic vector for the C, not equal to we compute the [ YTY] the inside M a characteristic vector vk with the worth k in characteristic, so use the u k the = compute a basic gather, among them k=1, … , M.As for this M a characteristic vector, make selection an important characteristic vector regard as their homologous and biggest characteristic value.Trains( test) the person a picture to the of M, characteristic vector W i the =[ w 1, w 2, … , w M'] uses the = u kT i, k= of w k 1, … , the M(6) computes.
For the sake of the person of the examination candidate for election whether a district is a real person or not a picture, also will make use of the formula(6) cast shadow the training( test) characteristic space inside to this candidate a district.Examination that cast shadow the district is a minimum distance to makes use of a person's face with not person's face examination district inside, passing the formula(7) come to something to realizes.Min(|| Wkcandidate － Wface||,|| Wkcandidate － Wnonface||),(7) among them the Wkcandidate is to trains( test) the characteristic space inside to the k a candidate a district, and Wface, Wnonface respectively is training( test) characteristic space middleman face with not person's face center sit the mark, but|| ×|| mean the characteristic in the space several in virtuous distance( Euclidean)
3.Person's face follows
In latest person an examination, pass to use a distance generous character standard to define the picture preface row in characteristic space inside a picture inside drive on the trail of person's face.For following a person's face, locate to is followed a person's face the characteristic vector is recent to is examined with the of K of person the of the an is several in the virtuous distance is to uses the obj= argkmin|| Wold － Wk||, k=1, … , K,(8) compute of.
After defining the person a district, locate to is examined person the center of a district with distance that hold the act center uses the distt( face, screen)= Facet( x, y) － Screen( height/2, width/2),(9) compute, among them Facet( x, y)
The that time a t inside were examined the person the center of a district, the Screen( height/2, width/2) is a center to hold the act district.Use this distance vector, can control the resemble to position in the machine with equilibrium/ tilt to one side of continuously time.The resembles the machine controller is what under such way work:Pass to control the activity resemble the machine even with/ tilt to one side the terrace examines drive of person a district keeps at hold the act central.In the table 2 oneself article mother country.What parameter mean is a control that activity resemble the machine.Mean with the false code equilibrium/ tilts to one side to handle continuously time resemble the fixed position of the machine with .
The calculation is even with/ tilt to one side keep on time with the false code that position:
Procedure Duration（x，y）
Begin
Sigd=None；
Distance=；
IF distance> then
Sigd=Close；
ELSEIF distance> then
Sigd=fat；
Return（Sigd）；
End Duration；
Procedure Orientation（x，y）
Begin
Sigo=None；
IF x> then
Add “RIGHT” to Sigo；
ELSEIF x<- then
Add “LEFT” to Sigo；
IF y> then
Add “up”to Sigo；
ElSEIF x<- then
Add “DOWN” to Sigo；
Return（Sigo）；
End Orientation；
4.Conclusion
It suggested in this text a kind of according to PAC solid contemporaries face examination with follow method.Were been a solid hour to proceed by this kind of method that suggest of, and the executive process is divided into two big part:Person's face identifies to follow with person's face.In first saw input flow, first, we make use of the infusion color, action the information is this type of to hint to examine the person a district with the PCA, then, use such way follow person's face:Passed a gearing namely equilibrium/ request the activity of the terrace resemble the machine examines drive of person a district keeps at hold the act central.The future work is a person who we will further develop this kind of method, passing from is examined a district grow to extract a characteristic to serve for a movable system..
[1] Z. Guo, H. Liu, Q. Wang, and J. Yang, “A Fast Algorithm of Face Detection for Driver Monitoring,” In Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, vol.2, pp.267 - 271, 2001.
[2] M. Yang, N. Ahuja, “Face Detection and Gesture Recognition for Human-Computer Interaction,” The International Series in Video Computing , vol.1, Springer, 2001.
[3] Y. Freund and R. E. Schapire, “A Decision-Theoretic Generaliztion of On-Line Learning and an Application to Boosting,” Journal of Computer and System Sciences, no. 55, pp. 119-139, 1997.
[4] J. I. Woodfill, G. Gordon, R. Buck, “Tyzx DeepSea High Speed Stereo Vision System,” In Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop, pp.41-45, 2004.
[5] Xilinx Inc., “Virtex-4 Data Sheets: Virtex-4 Family Overview,” Sep. 2008. DOI= http://www.xilinx.com/
[6] Y. Wei, X. Bing, and C. Chareonsak, “FPGA implementation of AdaBoost algorithm for detection of face biometrics,” In Proceedings of IEEE International Workshop Biomedical Circuits and Systems, page S1, 2004.
[7] M. Yang, Y. Wu, J. Crenshaw, B. Augustine, and R. Mareachen, “Face detection for automatic exposure control in handheld camera,” In Proceedings of IEEE international Conference on Computer Vision System, pp.17, 206.
[8] V. Nair, P. Laprise, and J. Clark, “An FPGA-based people detection system,” EURASIP Journal of Applied Signal Processing, 2005(7), pp. 1047-1061, 2005
[9] C. Gao and S. Lu, “Novel FPGA based Haar classifier face detection algorithm acceleration,” In Proceedings of International Conference on Field Programmable Logic and Applications, 2008.
外文翻译考核表
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