Lip Contour Extraction Based on Support Vector Machine

Transcription

1 Lip Cotour Extractio Based o Support Vector Machie Author Pa, Xiaosheg, Kog, Jiagpig, Liew, Ala Wee-Chug Published 008 Coferece Title CISP 008 : Proceedigs, First Iteratioal Cogress o Image ad Sigal Processig DOI Copyright Statemet 008 IEEE. Persoal use of this material is permitted. However, permissio to reprit/ republish this material for advertisig or promotioal purposes or for creatig ew collective works for resale or redistributio to servers or lists, or to reuse ay copyrighted compoet of this work i other works must be obtaied from the IEEE. Dowloaded from Lik to published versio Griffith Research Olie

2 008 Cogress o Image ad Sigal Processig Lip Cotour Extractio Based o Support Vector Machie Xiaosheg Pa Jiagpig Kog Ala Wee-Chug Liew Pekig Uiversity, Chiese Uiversity of Hog Kog, Griffith Uiversity Tiachi03@63.com Abstract Lip cotour extractio was a useful techique for obtaiig a mouth shape i a image, ad was oe of the most importat techiques for huma-machie iterface applicatios, such as lip readig ad speech recogitio. I this paper, a ew method to extract lip cotour from video was propose based o the fact that the lip color ad ski-color was varied i the differet color spaces. We first extracted frames from digital video first; the we classified face ito lip area ad o-lip area by the Support Vector Machie. At last we obtaied some parameters from the lip area to recostruct the lip cotour. The experimet results proved that the proposed method was accurate ad robust. Keywords: Lip cotour, Segmet, Color space, Support Vector Machie. Itroductio Cotiuous lip iformatio from a speaker is useful i speech recogitio system especially i a oisy eviromet. I color image, we usually use lip ad ski color iformatio to separate lip ad o-lip area. May image segmetatio techiques have bee proposed[][][3][7]. For color image segmetatio, histogram-based ad clusterig-based methods have bee widely used. I [], [3]Cheg et al. proposed a histogram segmetatio techique which ivolves performig a fuzzy partitio o a two-dimesioal(- D) histogram based o the maximum fuzzy etropy priciple. I [7], ala et al. proposed a segmetatio method which use fuzzy clusterig ad also take accout for spatial iformatio. There are two mai problems i lip segmet. First, the lip color is similar with togue color, so ier cotour of the lip is difficult to separate from the togue. Secod, the outer cotour of lip is ofte blurred due to the movemet of the lip. The algorithm we proposed ca successfully resolve the secod problem but ot the first oe. This paper is orgaized i four sectios. Sectio two describes the approach proposed here. Sectio three explais the experimetatio setup for testig the approach ad gives the results o classificatio. Sectio four outlies major coclusios as well as gives directios for future work.. The method.. Color Space As we kow, there are may kids of color spaces that have bee provided i existig research work, such as RGB, HSV, CMY, ad Chromatic color space[4]. The origial lip images are i the RGB color format. It is well kow that the value [R G B] of the color image i the color space RGB ot oly represet chromiace but also lumiace; they have high correlatio. I most cases, mappig the origial image ito a adaptive color space ca let the feature extractio task easier to be take. After the compariso of the color spaces listed above, the chromatic color space is chose as the color space applied i this paper. Three approximately color spaces are the 976 CIELAB color space ( Lab,, ), the 976 CIELUV color space ( Luv,, ) ad YIQ color space ( Y, I, Q ). Every color compoet of lip image ca be calculate by equatio from[4]. Every color compoet of lip image is list i Figure.. Because the compoet L i CIELAB ad CIELUV has same value, so we oly chose oe of them. It is also foud that the lip cotour of the compoet v i color space CIELUV ad the compoet b i color space CIELAB ca be directly observed dissimilar with the origial oe, i particular i the area of the lip corers. This paper is supported by NSFC. No /08 $ IEEE DOI 0.09/CISP

3 .. Histogram of lip ad o-lip area Figure. Differet color compoet of lip image We kow histogram represets the color distributio of lip ad o-lip area. Figure. ad Figure.3 show the lip area ad o-lip area of differet color compoets. The histograms i the left colum represet the o-lip area pixel distributio, whereas that i the right colum idicated the lip area pixel distributio. If the overlap betwee the pixel distractio histogram of the lip area ad that of the o-lip area exceeds a certai magitude, the it is very difficult to distiguish lip ad o-lip areas. The histograms of feature v have some overlaps betwee lip ad o-lip area. Thus it is ot useful for lip cotour extractio, so is that of feature b. The feature vector defied as below: F = { L, u, a, Y, I, Q} ().3. Support Vector Machie Figure. the histogram of color compoet Figure.3 the histogram of color compoet Support Vector Machie (SVM) has recetly bee successfully applied to a umber of applicatios ragig from face idetificatio to text categorizatio. The approach is systematic ad motivated by statistical learig theory[5]. SVM are based o the structural risk miimizatio priciple, closely related to regularizatio theory. Here we focus o SVM for twoclass classificatio. The mathematic expressio of SVM is: mi φ ( wb, ) = ( ww i ) () w subject to the costraits: yi ( wi xi + b), i =,..., (3) Ad the learig task ca be reduced to miimizatio of primal Lagragia: L= ( wiw ) ai ( yi ( wi x+ b ) ) (4) Where ai are Lagragia multipliers, hece ai 0. Takig the derivatives with respect to b ad w ad resubstitutig back the primal gives the Wolfe dual Lagragia: W( a) = ai aa i jyiyj( xii xj) (5) which must be maximized with respect to ai subject to the costrait: ai 0 ay i i = 0 (6) The decisio fuctio is the give by:

4 f( x) = sg(( wi x) + b) * * sg( ay i i( xi x) b) = i + (7) A SVM is maily characterized by its kerel fuctio. I experimet, we use liear kerel fuctio with parameter C=. 3. Implemetatio ad result The lip video is provided by the Liguistics Lab of Departmet of Chiese Laguage ad Literature of Pekig Uiversity. The lip cotour extractio procedure is as followig: As its origi is at (0,0), x [ ww, ]. The parameter s shows the skewess of lip shape. The skewig is attaied with the ways of usig the s trasformatio matrix 0. The expoet δ describes the deviatio of y from a quadratic curve ad it allows lip with differet degree of curvature to be captured accurately. The we extract parameters from middle colum ad use equatios (8) ad (9) to get the lip cotour. The result of lip cotour is list i the right colum of Figure.4. Step. Get frames from the video. Step. Mark lip cotour by hadwork[6], separate the face ito two compoet, lip ad o-lip. Ad obtai the axes of every pixel. Step3. Map the origi lip images ito other color space ad extract the feature vector F. Step4. Select appropriate pixels as traiig data[8], the use SVM classify the lip images. Step5. Extract parameters from lip area ad use lip model provided from [6] to obtai a lip cotour with smooth edge. The result of the lip segmet from the cotiuous frame is show i Figure.4. The left colum is four cotiuous frames extract from video. The middle colum is the result of we classified the face ito lip area ad o-lip area by the Support Vector Machie. We are ot very satisfied with the result. Because the lip edges we get are ot smooth eough, ad it is foud that the o-lip area i the lip corers with dark color is misidetified as lip area. It is also the case for the cocave o-lip area immediately below the uder lip. Because the color i those positio is more similar to lip area. Ala et al. provide a lip model[6], the equatios describig the lip shape Figure.5 are give by: h y ( ) = x sy xoff + h (8) ( w x ) off Figure.4 The left colum is the lip image sequece of speakig. The middle colum is correspodig the results of classify face ito lip ad o-lip area. The right colum is the lip cotour optimized by lip model. y +δ x sy = h w (9) Figure.5 Lip model

5 4. Discussio There still somethig ca be doe to improve the accuracy of my lip segmet program. For example ier lip cotour is still ca ot be extracted for the togue color is similar to lip color, ad get ier lip cotour is our ext stage target. Ad make some preprocessig to origi image maybe ca elimiate the shadow at the uderlip. The the result of lip segmet will be improved probably. Whe usig Support Vector Machie, we eed to choose a kerel fuctio, ad decide the parameter values to kerel fuctio. There is still o theory to prove which kerel fuctio is best, so we eed to do more experimet to fid out which kerel fuctio is the most suitable for our algorithm. 5. Referece []Lewis TW, Powers D M, Lip Feature Extractio Usig Red Exclusio, I Proc. Selected papers from Pa-Sydey Workshop o Visual Iformatio Processig, 000, pp []. H. D. Cheg, Y. H. Che, ad X. H. Jiag, Thresholdig usig two dimesioal histogram ad fuzzy etropy priciple, IEEE Tras. Image Processig, vol. 9, pp , Apr [3] H. D. Cheg, J. R. Che, ad J. Li, Threshold selectio based o fuzzy C-partitio etropy approach, Patter Recog., vol. 3, o. 7, pp , 998 [4]Adria Ford ad Ala Roberts, Colour Space Coversios, August, 998 (coloureq.pdf) [5] V. N. Vapik. Statistical Learig Theory. Wiley, 998. [6] A.W.C. Liew, S.H. Leug, W.H. Lau, Lip cotour. extractio usig a deformable model, I Proc. IEEE ICIP, Vol., pp , 000. [7]Liew A, Leug S H,Lau W H, et al. Segmetatio of Color Lip Images by Spatial Fuzzy Clusterig[J].IEEE Tras. o Fuzzy Systems,003,(4): [8] 边肇琪张学工等编著 模式识别 第二版清华大学出版社 00 年 0 月