人臉識(shí)別外文翻譯

1、畢業(yè)設(shè)計(jì)（論文）外文文獻(xiàn)翻譯題 目 基干PAC的實(shí)時(shí)人臉檢測(cè)和跟蹤方法專(zhuān)業(yè)名稱(chēng)測(cè)控技術(shù)與儀器學(xué)生姓名劉夢(mèng)丹指導(dǎo)教師任安虎畢業(yè)時(shí)間基于PAC的實(shí)時(shí)人臉檢測(cè)和跟蹤方法視頻信號(hào)處理有許多應(yīng)用，例如鑒于通訊可視化的電信會(huì)議，為殘疾人服務(wù)的唇讀系統(tǒng)。在上面提到的許多系統(tǒng)中，人臉的檢測(cè)喝跟蹤視必不可缺的組成部分。在本文中，涉及到一些實(shí)時(shí)的人臉區(qū)域跟蹤1-3。一般來(lái)說(shuō)，根據(jù)跟蹤角度的不同， 可以把跟蹤方法分為兩類(lèi)。有一部分人把人臉跟蹤分為基于識(shí)別的跟蹤喝基于動(dòng)作的跟蹤，而其他一部分人則把人臉跟蹤分為基于邊緣的跟蹤和基于區(qū)域的跟蹤4?；谧R(shí)別的跟蹤是真正地以對(duì)象識(shí)別技術(shù)為基礎(chǔ)的，而跟蹤系統(tǒng)的性能是受到識(shí)別方法

2、的效率的限制。基于動(dòng)作的跟蹤是依賴(lài)于動(dòng)作檢測(cè)技術(shù)，且該技術(shù)可以被分成視頻流（optical flow）的（檢測(cè)）方法和動(dòng)作一能量（motion - energy） 的（檢測(cè)）方法。基于邊緣的（跟蹤） 方法用于跟蹤一幅圖像序列的邊緣，而這些邊緣通常是主要對(duì)象的邊界線。然而， 因?yàn)楸桓櫟膶?duì)象必須在色彩和光照條件下顯示出明顯的邊緣變化，所以這些方法會(huì)遭遇到彩色和光照的變化。此外， 當(dāng)一幅圖像的背景有很明顯的邊緣時(shí)，（跟蹤方法）很難提供可靠的（跟蹤）結(jié)果。當(dāng)前很多的文獻(xiàn)都涉及到的這類(lèi)方法時(shí)源于 Kasset al.在蛇形匯率波動(dòng)5的成就。因?yàn)橐曨l情景是從包含了多種多樣噪音的實(shí)時(shí)攝像機(jī)中獲得的，因此許

3、多系統(tǒng)很難得到可靠的人臉跟蹤結(jié)果。許多最新的人臉跟蹤的研究都遇到了最在背景噪音的問(wèn)題，且研究都傾向于跟蹤未經(jīng)證實(shí)的人臉，例如臂和手。在本文中，我們提出了一種基于PCA的實(shí)時(shí)人臉檢測(cè)和跟蹤方法，該方法是利用一個(gè)如圖1 所示的活動(dòng)攝像機(jī)來(lái)檢測(cè)和識(shí)別人臉的。這種方法由兩大步驟構(gòu) 成：人臉檢測(cè)和人臉跟蹤。利用兩副連續(xù)的幀，首先檢驗(yàn)人臉的候選區(qū)域，并利用PCA技術(shù)來(lái)判定真正的人臉區(qū)域。然后，利用特征技術(shù)（eigentechnique） 跟蹤被證實(shí)的人臉。1. 人臉檢測(cè)在這一部分中，將介紹本文提及到的方法中的用于檢測(cè)人臉的技術(shù)。為了改進(jìn)人臉檢測(cè)的精確性，我們把諸如膚色模型1,6和PCA7,8修些已經(jīng)發(fā)表的

4、技術(shù)結(jié)合起來(lái)。膚色分類(lèi)檢測(cè)膚色像素提供了一種檢測(cè)和跟蹤人臉的可靠方法。因?yàn)橥ㄟ^(guò)許多視頻攝像機(jī)得到的一幅RGB圖像不僅包含色彩還包含亮度，所以這個(gè)色彩空間不是檢 測(cè)膚色像素1,6的最佳色彩圖像。通過(guò)亮度區(qū)分一個(gè)彩色像素的三個(gè)成分， 可以 移動(dòng)亮度。人臉的色彩分布是在一個(gè)小的彩色的色彩空間中成群的，且可以通過(guò)一個(gè)2維的高斯分部來(lái)近似。因此，通過(guò)一個(gè)2維高斯模型可以近似這個(gè)膚色模 型，其中平均值和變化如下：-1£.Dm （r g） 其中 N白白 乙一旦建好了膚色模型，一個(gè)定位人臉的簡(jiǎn)單方法是匹配輸入圖像來(lái)尋找圖像 中人臉的色彩群。原始圖像的每一個(gè)像素被轉(zhuǎn)變?yōu)椴噬纳士臻g，然后與該膚 色模

5、型的分布比較。動(dòng)作檢測(cè)雖然膚色在特征的應(yīng)用種非常廣泛，但是當(dāng)膚色同時(shí)出現(xiàn)在背景區(qū)域和人的 皮膚區(qū)域時(shí)，膚色就不適合于人臉檢測(cè)了。利用動(dòng)作信息可以有效地去除這個(gè)缺 點(diǎn)。為了精確，在膚色分類(lèi)后，僅考慮包含動(dòng)作的膚色區(qū)域。結(jié)果，結(jié)合膚色模 型的動(dòng)作信息導(dǎo)出了一幅包含情景（人臉區(qū)域）和背景（非人臉區(qū)域）的二進(jìn)制 圖像。這幅二進(jìn)制圖像定義為，其中It（x,y）和It-1（x,y）分別是當(dāng)前幀和前面那幀中像素（x,y）的亮度。St是當(dāng)前幀中膚色像素的集合，（斯坦）t是利用適當(dāng) 的閾限技術(shù)計(jì)算出的閾限值9。作為一個(gè)加速處理的過(guò)程，我們利用形態(tài)學(xué)（上） 的操作（morphological operations

6、）和連接成分分析，簡(jiǎn)化了圖像 Mt。利用PCA檢驗(yàn)人臉因?yàn)橛性S多移動(dòng)的對(duì)象，所以按序跟蹤人臉的主要部分是很困難的。止匕外， 還需要檢驗(yàn)這個(gè)移動(dòng)的對(duì)象是人臉還是非人臉。我們使用特征空間中候選區(qū)域的 分量向量來(lái)為人臉檢驗(yàn)問(wèn)題服務(wù)。為了減少該特征空間的維度，我們把 N維的 候選人臉圖像投影到較低維度的特征空間，我們稱(chēng)之為特征空間或人臉空間 7,8。在特征空間中，每個(gè)特征說(shuō)明了人臉圖像中不同的變化。為了簡(jiǎn)述這個(gè)特征空間，假設(shè)一個(gè)圖像集合 Ii,I3, , Im,其中每幅圖 像是一個(gè)N維的列向量，并以此構(gòu)成人臉空間。這個(gè)訓(xùn)練（測(cè)試）集的平均值 用A=1/M 2Mi-1 li來(lái)定義。用i Ii A來(lái)計(jì)算每

7、一維的零平均數(shù)，并以此構(gòu)成一 個(gè)新的向量。為了計(jì)算 M的直交向量，其中該向量是用來(lái)最佳地描述人臉圖像M M“生小任 c 7r i iir YYr + I ej、+4 一分布，首先，使用 M i r i(4)來(lái)計(jì)算協(xié)方差矩陣Y - m。雖然矩陣C是NXN維的，但是定義一個(gè)N維的特征向量和N個(gè)特征值是個(gè)難處來(lái)計(jì)Y VKUk 理的問(wèn)題。因此，為了計(jì)算的可行性，與其為 C找出特征向量，不如我們計(jì)算Y T Y中個(gè)M特征向量VK和特征值k ,所以用算一個(gè) 基本集合，其中k=1, , Mo關(guān)于這M個(gè)特征向量，選定M個(gè)重要的 特征向量當(dāng)作它們的相應(yīng)的最大特征值。對(duì)于M個(gè)訓(xùn)練(測(cè)試)人臉圖像，特征向量 W i=

8、wi, W2, , , wm用 wK ukT i,k=1, M (6)來(lái)計(jì)算。為了檢驗(yàn)候選的人臉區(qū)域是否是真正的人臉圖像，也會(huì)利用公式(6)把這個(gè)候選人臉區(qū)域投影到訓(xùn)練(測(cè)試)特征空間中。投影區(qū)域的檢驗(yàn)是利用人臉類(lèi) 和非人臉類(lèi)的檢測(cè)區(qū)域內(nèi)的最小距離，通過(guò)公式(7 )來(lái)實(shí)現(xiàn)的。|_ _ _ candidate _ _ _ _ _ candidate _ _ _、 、,> 匚 r candidate (r 、r r / -/ 、r t 、卜、WkWface | , WkiWnonface1),(7)其中W*didate是訓(xùn)練(測(cè)試)特征空間中對(duì)k個(gè)候選人臉區(qū)域，且Wface, Wnonfac

9、e分別是訓(xùn)練(測(cè)試)特征空間中 人臉類(lèi)和非人臉類(lèi)的中心坐標(biāo)，而 | X|表示特征空間中的歐幾里德距離(Euclidean)。3.人臉跟蹤在最新的人臉檢測(cè)中，通過(guò)在特征空間中使用一個(gè)距離度量標(biāo)準(zhǔn)來(lái)定義圖像 序列中下一幅圖像中被跟蹤的人臉。為了跟蹤人臉，位于被跟蹤人臉的特征向量和K個(gè)最近被檢測(cè)的人臉之間的歐幾里彳惠距離是用obj = argkmin|Wold Wk| ,k=1, , , K, (8)來(lái)計(jì)算的。在定義了人臉區(qū)域后，位于被檢測(cè)人臉區(qū)域的中心和屏幕中心之間的距離用 distt (face, screen) = Face (x, y) Screen (height/2, width/2),

10、 (9)來(lái)計(jì)算， 其中Facet (x, y)是時(shí)間t內(nèi)被檢測(cè)人臉區(qū)域的中心，Screen(height/2, width/2)是屏幕的中心區(qū)域。使用這個(gè)距離向量，就能控制攝像機(jī)中定位和平衡/傾斜的持續(xù)時(shí)間。攝像機(jī)控制器是在這樣的方式下工作的：通過(guò)控制活動(dòng)攝像機(jī)的平和 /傾斜平臺(tái)把被檢測(cè)的人臉區(qū)域保持在屏幕的中央。在表2自己品母國(guó)。參數(shù)表示的是活動(dòng)攝像機(jī)的控制。用偽代碼來(lái)表示平衡/傾斜處理的持續(xù)時(shí)間和攝像機(jī)的定位。計(jì)算平和/傾斜持續(xù)時(shí)間和定位的偽代碼：Procedure Duration (x, y)BeginSicd=None；22Distance=xy ;IF distancecios e

11、 thenSig=ClosqELSEIF distances 0 thenSig=fat;Return (Sig);End Duration；Procedure Orientation (x, y)BeginSigo=NoneIF x> Ox thenAdd “RIGHT tooSigELSEIF x<x thenAdd “LEFT' too SigIF y> 0 y thenAdd “up” tOcSigElSEIF x< -ythenAdd “ DOW/N to SigReturn (Si");End Orientation；4. 結(jié)論本文中提議了

12、一種基于 PAC的實(shí)時(shí)人臉檢測(cè)和跟蹤方法。被提議的這種方 法是實(shí)時(shí)進(jìn)行的，且執(zhí)行的過(guò)程分為兩大部分：人臉識(shí)別和人臉跟蹤。在一個(gè)視 頻輸入流中，首先，我們利用注入色彩、動(dòng)作信息和PCA這類(lèi)提示來(lái)檢測(cè)人臉區(qū)域，然后，用這樣的方式跟蹤人臉：即通過(guò)一個(gè)安裝了平衡/請(qǐng)求平臺(tái)的活動(dòng)攝像機(jī)把被檢測(cè)的人臉區(qū)域保持在屏幕的中央。未來(lái)的工作是我們將進(jìn)一步發(fā)展這種方法，通過(guò)從被檢測(cè)的人臉區(qū)域種萃取臉部特征來(lái)為臉部活動(dòng)系統(tǒng)服務(wù)。譯文：PCA-Base Real-Time Face Detection and TrackingSeeing the signal of handles many applications,

13、 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

14、 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 accord

15、ing 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,

16、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 meth

17、od that act the energy( motion energy).According to the method of the edge used for the edge that follow a picture preface row, but these edges is usually the boundary line of the main , because were musted shine on with the light at the color by the on the trail of object the term descends to displ

18、ay the obvious edge changes, so these methods will fall among the color with the variety that light shine addition, be a background of picture contain very obvious edge,( follow the method) dependable result in very difficult this type of method that a lot of cultural heritages all involve come from

19、 the Kasset the snake form rate of exchange motion 5 the achievement of 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 latest a research for followi

20、ngs 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

21、make use of a,such as figure,1 show resemble machine to examine with identify the person kind of method from two greatest steps composing:Person an examination with person's face two pairs of consecutive frames, examine a person's face candidate for election districts first, combine exploita

22、tion PCA technique to judge the real person a , make use of the characteristic technique( eigen technique) follow to confirmed person's face.1. Person an examinationIn this first part, will introduce the method that this text mention inside of used for the technique that examine person's imp

23、roves 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.skin color classificationThe examination skin color pixel provides a kind of examination with follow the facial and dependable method in pass many that sees th

24、e 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 the brightness distinguish the three components of a color pixel,brightness can be Gauss for of color distributing is in a

25、 small chromatic color space large groups, and can passing first 2 cent department to look , pass a 2 Gauss models can look like this skin color model, among them average value with change as follows:Once set up to like the skin color model, a positions facial and simple method in person is match th

26、e importation picture to look for facial color in middleman in picture a pixel of the primitive picture were changed into the chromatic color space, then distributing with the skin color's model the comparison.action examinationAlthough the skin color application in characteristic grows very ext

27、ensive, 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 use of to act information can away with this weakness the sake of the precision, after the skin color divides into section, consider the

28、 skin color district of the containment action , 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) binary system picture definition is, among them It( x, y) With the It-1( x

29、, 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( m

30、orphological operations) that appearance learn( top) with link the composition analyzes, simplifying the picture Mt.make use of the PCA examine person's faceThere is many ambulatory objects, so follow in sequence the facial and main part in person is very addition, return the demand examine this

31、 ambulatory object is person's face or not person's uses characteristic space inside the weight vector of the candidate for election district to behave face examination problem reducing that characteristic the spatial a candidate for, we N a picture casts shadow the characteristic space of t

32、he 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.In order to sketch the feature space, suppose a picture gather the Ii, I2, I3, , , I m, among them each pi

33、cture is the row vector of a N , and with this composing person a average value that this training( test) gather uses theA 1M M11 ithe i I i A computes the zero average number of each , and with this composing a new computing the M keep handing over vector, among them that vector is to uses to come

34、to describe the personM _1r rCi i YYrbest a picture ground distribute, first, the usage M(4) computeto help the covariance matrix Y 1 2 M .Although matrix C is N x N dimension, but define a N dimensional feature vector and the N eigenvalues is a difficult , for the sake of calculating possibility, w

35、ith 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 Y VK worth k in characteristic, so use theuk 一compute a basic gather, among them k=1, , , for this M a characteristic vector, make selection an important characteri

36、stic vector regard as their homologous and biggest characteristic ( test) the person a picture to the of M, characteristic vector the W i = w 1, w 2, , , w m, uses the W k= uK i , k=1, , , the M(6) computes.For the sake of the person of the examination candidate for election whether a district is a

37、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 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 t

38、he formula(7) come to something to (| W：andidate Wfacell,|l Wcandidate Wnonfacell)， among them the Wfandidateis 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

39、9;s face center sit the mark, but| x |mean the characteristic in the space several in virtuous distance( Euclidean).'s face followsIn 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

40、 drive on the trail of person's 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= argk min| W 01d Wk|, k=1, , , K,(8) compute of

41、.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)= Face( x, y) Screen( height/2, width/2),(9) compute, among them Face( x, y) The that time a t inside were examined the person the center

42、of a district, the Screen( height/2, width/2) is a center to hold the act this distance vector, can control the resemble to position in the machine with equilibrium/ tilt to one side of continuously 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 the table 2 oneself article mother parameter mean i