Face Tracking Using Motion-Guided Dynamic Template Matching

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1 ACCV2002: The 5th Asan Conference on Computer Vson, January 2002, Melbourne, Australa. Face Trackng Usng Moton-Guded Dynamc Template Matchng Lang Wang, Tenu Tan, Wemng Hu atonal Laboratory of Pattern Recognton Insttute of Automaton, Chnese Academy of Scences, Bejng, P. R. Chna, E-mals: {lwang, tnt, Abstract Combnng two sophstcated technques of moton detecton and template matchng, ths paper proposes a smple but effectve algorthm for detecton and trackng of human faces. Frst, we use a statstcal model of skn color and shape nformaton to detect face n the frst frame, and ntalze t as an appearance-based ntensty template for subsequent trackng. Second, ncorporatng background subtracton, projecton hstograms of movng slhouette and geometrc constrants of body parts, we can quckly determne a good approxmaton of the search regon correspondng to head locaton. Fnally, a correlaton-based template matchng procedure s appled to further localze human face accurately, and current template can be dynamcally updated n sze and content to adapt temporal changes of the tracked face s scale and orentaton. Moreover, a confdence measure representng the template s relablty s presented to gude possble template re-ntalzaton for contnuous face trackng. Expermental results demonstrate the valdty of our proposed method.. Introducton Image analyss of faces has been an actve research topc n computer vson and mage processng. Ths strong nterest s drven by some promsng applcatons such as survellance and securty montorng, advanced human-machne nterface, vdeo conferencng and vrtual realty. Generally speakng, major research areas nclude face detecton, trackng and recognton, face anmaton, expresson analyss, lp readng, etc. As the bass for all other related mage analyss of human faces, face detecton and trackng are of great mportance. Recently, there have been consderable research achevements n detecton, recognton and trackng of human faces [~2]. For example, Rowley et al. [] proposed a neural network based algorthm for face detecton, and Garca and Tzrtas [2] used quantfed skn color regons mergng and wavelet packet analyss n face detecton. More face detecton and recognton algorthms can be found n a revew [3]. Instead of detectng human faces n each frame ndependently, face trackng utlzes temporal correlaton to locate them. Presently, most researchers have emphaszed on color-based [4,7~2] or model-based trackng [5~6]. For nstance, Yang and Wabel [4] bult a real tme face trackng system based on the normalzed color space, and Colmenarez et al. [5], DeCarlo and Metaxas [6] used a 3D face model n face trackng process. However, these algorthms seldom deal wth multple faces effectvely, especally occluson. Furthermore, these algorthms are usually computatonally complex due to ther use of color correlaton, blob growng, Kalman flter predcton, 3D model, etc. In ths paper, based on an effcent combnaton of moton detecton and template matchng, we develop a much smpler algorthm for face trackng whch acheves hghly satsfactory trackng performance. It s well known that the trackng propertes of moton detecton and template matchng are complementary. In other words, movng targets can be precsely tracked usng dynamc template matchng guded by moton detecton. Therefore, ths motvates us to present a smpler procedure for face detecton and trackng. It can be smply descrbed as follows: frst, a statstcal model of skn color and shape nformaton are used to extract the face n the frst frame, and the detected face s ntalzed as an appearance-based ntensty template for later trackng; second, ncorporatng background subtracton, projecton hstograms of movng slhouette and structural constrants of body parts, we can determne a sutable search regon correspondng to an approxmaton of head locaton quckly; fnally, a correlaton-based template matchng procedure s appled to fnd the best match as the refned face poston, and the current template can be dynamcally updated n sze and content to cope wth temporal changes of the face s scale and orentaton. Expermental results demonstrate the algorthm s effectveness n face detecton and trackng. The man contrbutons of our algorthm nclude: ) The use of moton detecton n gudng correlaton-based template matchng prevents the template drftng onto Page

2 background; 2) The use of computatonally expensve Kalman flterng, 3D model, or other probablstc approaches s avoded; 3) Incorporatng projecton hstograms of movng slhouette wth constrants on body parts can sgnfcantly narrow down the search range; 4) Except for template ntalzaton, the proposed algorthm s mostly performed on the transformed grayscale mages, whch ncreases the lkelhood of real-tme face trackng. The remander of ths paper s organzed as follows. Secton 2 outlnes the algorthm of face trackng. Face detecton usng the skn color model and shape nformaton s ntroduced n Secton 3. Secton 4 descrbes the face trackng process, ncludng moton segmentaton, determnaton of search regons, and template matchng and updatng. Expermental results are presented and dscussed n Secton Overvew of our algorthm Our face trackng system shown n Fgure conssts of three major processng modules: template ntalzaton, determnaton of search regons, and dynamc template matchng and updatng. Template ntalzaton can be thought equvalently as a face detecton problem. In our approach, we choose the normalzed color space [4] to represent skn-lke regons. The parameters of the skn color model can be estmated usng the maxmum lkelhood estmaton (MLE) method. Once human face s detected, t wll be converted nto an An nput sequence Template Confdence Y Measure > Threshold Y Moton Detecton Projecton Hstogram Set Search Regon Template Matchng Template Updatng Template Intalzaton appearance-based ntensty template for subsequent Face Detecton The frst frame n an mage sequence Fgure. Block dagram of face trackng algorthm trackng. Usng background subtracton, regons of change can be fast segmented from the background constructed by the least medan of squares (LMedS) method [3]. Meanwhle, projecton hstograms of movng slhouette and body parts constrants are utlzed to determne the correspondng search space. In the last module, a template matchng process based on the sum of squared dfference (SSD) dstance measure between the template and a search regon s performed to accurately localze face poston n current mage. Also, the template s dynamcally updated usng an nfnte mpulse response (IIR) flter [6] to adapt to ts sze and content changes n response to face movement along sequental frames. In addton, we defne a template confdence measure to bootstrap the re-ntalzaton of the template for contnuous and relable trackng. 3. Face detecton As far as face detecton s concerned, many approaches have been proposed usng texture, shape, and color nformaton or ther combnatons [2,4,7~2]. For smplcty, we only use color and shape nformaton to realze face detecton. 3.. Skn Color Model Color s a smple but mportant pxel-based feature to detect human faces. At present, there have been varous color representatons for skn. For example, Ohya et al. [9] represented skn regon usng LUV color space, and Sobottka and Ptas [] used HSV color space and shape nformaton to extract facal regons. In ths paper, we choose the smple and effcent normalzed r-g color space descrbed by Yang and Wabel [4]. In ther studes of skn-color dstrbutons, three conclusons could be obtaned [4] : a) skn color dstrbutons of dfferent people are clustered n a relatvely small area wthn the chromatc space. That s, skn colors of dfferent people are very close, but they dffer dstnctly n ntenstes; b) skn color dfferences among dfferent people can be reduced by ntensty normalzaton; c) under certan lghtng condton, skn-color dstrbuton can be characterzed by a multvarate normal dstrbuton. Assumng that a face color dstrbuton s represented by 2D Gaussan model (M, 2 ), we can learn the parameters of the mean and covarance matrx usng Maxmum Lkelhood Estmaton (MLE). T M = ( r, g ) = X () m m = Σ = ( X M )( X M ) (2) = Where X, and the superscrpt T are the th sample, the total number of tranng samples and transpose, T Page 2

3 respectvely. The procedure for creatng skn color model can be descrbed as follows [4] : ) take a set of mages and select skn-colored regon nteractvely; 2) estmate the mean and the covarance of the color dstrbuton n the normalzed color space; 3) substtute the estmated parameters nto the Gaussan dstrbuton model. Snce the model only has sx parameters, t s easy to adjust them to dfferent people and lghtng condtons Face template ntalzaton Frst, the normalzed r-g values of each pxel n an nput mage are converted nto the color dstance from standard skn color, whch s measured by Mahalanobs dstance denoted by the followng equaton, where the vector X =(r, g ) T. T D = ( X M ) ( X ) (3) ( ) M are defned as follows, and all regons wth any of C, S and O lower than the correspondng predefned thresholds are removed. A A Dy C =, S =, 2 P D x D O = (4) y Dx An example of face detecton usng color and shape nformaton s shown n Fgure 2, from whch we show that the te smlar to skn-color and both hands are effectvely removed. The detected face s ntalzed as an ntensty template for subsequent trackng. 4. Face trackng We utlze the coarse-to-fne strategy for face trackng. Frst, we determne an approxmaton of search regon at a large scale correspondng to head poston through moton detecton. Then, the dynamc template representng temporal changng nformaton among mage frames s matched n search regons to further localze face poston accurately. 4.. Determnaton of search regon (a) (c) (d) Fgure 2. Face detecton (a) An orgnal mage; (b) The result after skn-color flterng; (c) The result after further shape flterng; (d) The fnal result. Then, we make a hstogram of color dstances from prevous results to extract skn-lke regons by a dscrmnant analyss. Fnally, some addtonal operatons such as sze and shape based flterng are performed to further extract face regons wth approprate sze. As we know, t s not always easy to locate human faces because the background may contan other skn-lke colors too. So, we should perform some post-processng to elmnate some solated skn-lke pxels and small blobs such as hands. Frst, we use connectvty and morphologcal flterng to elmnate some solated ponts and noses. Then, we apply a connected component analyss at a coarse resoluton. Fnally the geometrc sze and shape features of human face are analyzed to further remove some non-face small blobs. The decson crtera rely on combnatons of the area A, the permeter P, and the sze (D x, D y ) of the boundng box of the connected component [0]. Several measures of shape nformaton (b) The determnaton of search regons ams at speedng up later trackng process. It nvolves the followng three steps. a) Moton Detecton: Background subtracton s a partcularly effcent method for detectng gray level changes. Generally, t s composed of the generaton of the background, the arthmetc subtracton operaton and the selecton of a sutable threshold. A potentally robust approach s to dynamcally generate background from some porton of mage sequence and perodcally update t to account for possble changes n the background. The least medan of squares (LMedS) method s adopted [3]. t Let I xy represent a sequence of collected mages, and (x, y) s the pxel locaton. The resultng background B xy can be computed by t 2 Bxy = mn b medt ( I xy b) (5) where b s background value to be determned. Then, a threshold mage T xy s determned from the medan absolute devaton at each pxel: T xy = MAD xy, where MAD xy =med t I t xy-b xy and the constant.4826 s a normalzaton factor wth respect to a Gaussan dstrbuton [3]. Fnally, the dfference mage D xy can be obtaned by comparng dfference values between the ncomng mage and the background aganst the threshold mage. t I xy Bxy Txy Dxy = (6) 0 Otherwse The segmented foreground regons probably lead to spurous pxels, holes nsde movng objects and other anomales, therefore they need to be further fltered usng Page 3

4 morphologcal operators. Fnally, a bnary connected component analyss s appled to clearly extract hghly concentrated movng regons. An example of change detecton s shown n Fgure 3 (a)~(d). b) Projecton Hstogram: Projecton hstogram has been shown to be useful n some computer vson systems. For example, Kuno [4] used shape features of movng slhouette patterns as the parameters to detect human n survellance system, and these shape features are manly the mean and the standard devaton of projecton hstograms of slhouette patterns. Also, W 4 system [5] determned whether the foreground regon contaned multple people by analyzng vertcal projecton hstogram of slhouettes. We also utlze projecton hstogram to represent the shape of a bnary slhouette. The horzontal and vertcal projecton hstograms, whch wll be used to determne search regons combnng constrants on body parts, can be easly computed by projectng the foreground regon on an axs perpendcular to major axs and along major axs of human body, respectvely. Fgure 3 (e)~(g) gve an example of projecton hstograms of movng slhouette. (a) (b) (c) (d) (e) (f) (g) (h) () Fgure 3. Determnaton of search regon (a) The background mage constructed by LMedS; (b) The orgnal mage; (c) The result after background subtracton; (d) The result after post-processng; (e) Movng slhouette; (f) and (g) The vertcal and horzontal projecton hstograms of movng slhouette; (h) The approxmate postons of major axs and the neck of human body; () The search regon supermposed by a whte rectangular box. c) Addng Body Constrants: The shape of human body s symmetrcal about ts major axs, and the head usually appears above the trunk. Therefore, ths topologcal structure wll show some specfc propertes n projecton hstograms of ts slhouette. We guess that the postons of body major axs and the neck should correspond to a local peak n the vertcal projecton hstogram and a local valley n the horzontal projecton hstogram, respectvely. The major axs s determned by fndng a local peak hgher than a threshold value selected as the mean value of the entre hstogram n the vertcal projecton hstogram [5]. Smlarly, we can determne the neck coordnate by searchng a local valley n the horzontal projecton hstogram. As we know, the aspect rato of human face s about /.2. Based on these assumptons, we can ntally set a search regon as an approxmate head locaton. Allowng for moton segmentaton errors, search regons should be sutably adjusted. On the one hand, f t s set too small, t may not completely nclude the tracked face. On the other hand, f t s set too bg, t wll accordngly ncrease the computatonal cost durng the matchng process. For accurate trackng, we expand the search range.5 tmes the ntally determned head regon usng projecton hstograms. A result of the determnaton of search regon s shown n Fgure 3 (h)~() Template matchng and updatng Generally, correlaton matchng s computatonally the most expensve part of the trackng algorthm. However, f t only need be performed n a relatvely small search regon, the computaton tme wll decrease sgnfcantly. Also, template matchng s based towards areas where moton s detected, so t s more lkely to prevent the template drftng onto background. a) Correlaton-based Matchng: All canddate search regons are used to match wth the current template so as to fnd the best matchng result, and several specal detals should be consdered: ) because of perodcal background updatng, a human wll be probably mssed out by moton detecton when he or she stops for a whle. So, an extra search regon should be used, whch s composed of the pxels n current frame n response to the locaton of CR n- ; 2) t s reasonable to assume that the locaton and sze of faces wthn each frame do not change much. Therefore, to mprove trackng speed, we only need to search possble faces n neghborng search regons guded by the dsplacement nformaton between search regons centers; 3) n general, there wll be a possble transent overlappng between two walkng fgures. If ther heads are solated clearly, we can stll accurately determne ther correspondng search regons by projecton hstograms. If ther heads are partally occluded, moton detecton can only provde a composte search regon that wll be used to match all templates to fnd one most sutable match. Certanly another occluded face can stll be tracked approxmately usng moton speed nformaton of the face and ts template s updated untl the transent occluson ends. To mantan a lock on the tracked object, the underlyng correlaton algorthms must be robust to scale changes. Ths can be solved by selectve template sze Page 4

5 adjustment before the matchng process. For each poston of search regon, a smlarty measure between the template and the search block s evaluated by the sum of the squared dfference (SSD) of grayscale value. For a dsplacement (u, v) between reference template and search block, the dfference at pxel poston (x, y) can be represented by 2 D ( u, v) = ( S( x, y, t) T( x + u, y + v, t )) (7) xy x, y where S (x, y, t) denotes the grayscale at poston (x, y) at tme nstance t, and T represents the template. The best match poston s found by choosng the mnmum dfference value greater than a predefned threshold. b) Template Updatng: The correlaton process has to deal wth ncremental changes of the tracked object s appearance, so t s expected that the template be updated to catch up wth the smooth varaton of face appearance from one frame to another. In ths paper, we use an nfnte mpulse response (IIR) flter to update the template [6]. Once a best correlaton match at the search block M n n current frame s found, t wll be merged wth prevous template T n- through an IIR flter to produce a new template T n for trackng n subsequent frames. Ths process of template updatng can be formulzed by Tn = α M n + ( α) Tn (8) where α s a tme constant that specfes how fast new for hs or her face trackng. 5. Expermental results Many experments have been carred out to evaluate the trackng performance. In our laboratory wth a relatvely complex background, the observed people are requred to move randomly n the vewable area, and a dgtal camera Panasonc V-DX00E fxed on a trpod s used to capture a group of true-color mage sequences wth the resoluton of These mages are then sub-sampled nto the resoluton of Our proposed algorthm s manly performed n two cases. One s sngle face trackng, another s multple faces trackng even n the presence of possble occluson. For sngle face trackng, our algorthm provdes a robust trackng result even n the presence of slght scale and orentaton changes of human face. Fgure 5 shows an example of sngle face trackng, where the bgger and smaller whte rectangular boxes represent the correspondng search regon and the tracked face respectvely. From Fgure 5, we can show that the trackng results are encouragng though the contnuous smooth changes of the face s sze and orentaton exst. Ths benefts greatly from the precse determnaton of search regon and dynamc updatng of template n response to face movement frame by frame. T n- IIR Flter T n M n Fgure 4. Template updatng nformaton replaces old observatons. An example of template updatng usng IIR flter s shown n Fgure 4. c) Template Confdence Measure: Many factors such as the accumulated errors of segmentaton and frequent adjustments of template sze maybe affect trackng process. To track human faces contnuously and relably, we defne a confdence measure to observe the relablty of the template. It s proportonal to the resoluton of the template and the matchng value of smlarty measure functon n template matchng process. Once t s lower than a sutable predefned threshold, that s, t s at too low resoluton to represent real mage content of the tracked face, skn-color based template ntalzaton must be bootstrapped agan. Furthermore, f a new human s detected by moton segmentaton, a new template ntalzaton should be performed accordngly Fgure 5. Sngle face trackng Trackng of multple faces s clearly more dffcult because these faces may occlude each other. Wthout loss of generalty, we consder the trackng of two faces n our experment. When the faces move n ther own trajectores ndependently, we can track them separately wth multple dynamc templates. When two human bodes meet but ther heads are separated, the determnaton of search regons s stll successful. When head occluson happens, the dffcultes of multple face trackng wll depend on several factors, such as how smlar these faces are, how long the occluson lasts, and at what percentage one face s occluded by another face. We assume that occluson s partal and transent. When the occluson happens, moton segmentaton only provdes a composte search regon. Therefore, the non-occluded face can stll be tracked by template Page 5

6 matchng, whle the trackng of the occluded face wll possbly be a temporal falure. However, we may turn to moton nformaton to approxmately estmate ts poston. Generally, t s reasonable to assume that the locaton and sze of faces wthn each frame do not change much. Therefore, we may smply utlze moton nformaton of face speed wth respect to the changng dstance between search regons centers among the neghborng frames to approxmately predct the occluded face s poston n next frame, and ts template s updated untl the transent occluson ends. Fgure 6 gves a successful example of two faces trackng n the presence of occluson, where only the trackng result of the person n jacket s shown for clarty. From Fgure 6, we can see that the trackng results before, durng and after occluson are very precse. Fgure 6. Face trackng n the presence of occluson Of course, our algorthm wll probably fal under some specal condtons such as the sudden sgnfcant changes of sze and orentaton of face movement. Ths s because that f face movement s too sudden, the template matchng and updatng wll be unrelable for representng face-changng nformaton. In a word, a large number of expermental results demonstrate that our trackng algorthm obtans hghly satsfactory performance. 6. Conclusons In ths paper, a novel face-trackng algorthm based on an effcent combnaton of template matchng and moton detecton s proposed. Usng moton detecton to gude template matchng can not only prevent the template drftng nto background, but also provde fast and robust trackng despte occluson wthout the requrement of havng a temporal predcton flter such as Kalman flter. We test the algorthm on some vdeo sequences, and expermental results show our algorthm works well. There are a number of drectons to mprove the algorthm. For example, we can choose better moton detecton methods to decrease segmentaton error, and use more moton nformaton to effectvely cope wth occluson n mult-face trackng n future work. Acknowledgements The authors would lke to thank Ms. Y. C. Fang for her valuable dscusson. Ths work s supported n part by SFC (Grant o ), and the Innovaton Fund of Insttute of Automaton (Grant o. M0J02), Chnese Academy of Scences. References [] H. Rowley, S. Baluja, and T. Kanade, eural etwork based Face Detecton, PAMI, 998, pp [2] C. Garca and G. Tzrtas, Face Detecton Usng Quantfed Skn Color Regons Mergng and Wavelet Packet Analyss, IEEE Trans. on Multmeda, (3), 999, pp [3] A. Samal and P.A. Iyengar, Automatc Recognton and Analyss of Human Faces and Facal Expressons: A Survey, Pattern Recognton, 25(), 992, pp [4] J. Yang and A. Wabel, A Real-Tme Face Tracker, WACV, 996, pp [5] A. Colmenarez, R. Lopez, and T. Huang, 3D Model-Based Head Trackng, VCIP, CA, 997. [6] D. DeCarlo and D. Metaxas, Deformable Model-Based Face Shape and Moton Estmaton, ICFG, 996. [7] A. Saber and A.M. Tekalp, Frontal-vew Face detecton and Facal Feature Extracton Usng Color, Shape and Symmetry Based Cost Functons, Pattern Recognton Letters, 9(8), June 998, pp [8] Gang Xu and T. Sugmoto, A Software-based System for Real-tme Face Detecton and Trackng Usng Pan-Tlt-Zoom Controllable Camera, ICPR, 998, pp [9] M. Ohya et al, Face Detecton System by Usng Color and Moton Informaton, ACCV, 2000, pp [0] B. Menser and M. Wen, Segmentaton and Trackng of Facal Regons n Color Image Sequences, VCIP, vol. 4067, Perth, Australa, June 2000, pp [] K. Sobottka and I. Ptas, A ovel Method for Automatc Face Segmentaton, Facal Feature Extracton and Trackng, Sgnal Processng: Image Communcaton, 2(3), June 998, pp [2] P. Feguth and D. Terzopoulos, Color-based Trackng of Heads and Other Moble Objects at Vdeo Frame Rates, CVPR, 997, pp [3] Y.H. Yang and M.D. Levne, The Background Prmal Sketch: An Approach for Trackng Movng Objects, Machne Vson and Applcatons, 5, 992, pp [4] Y. Kuno et al, Automated Detecton of Human for Vsual Survellance System, ICPR, 996, pp [5] I. Hartaoglu, D. Harwood, and L. Davs, W4: Real-Tme Survellance of People and Ther Actvtes, PAMI, 22(8), 2000, pp [6] A Lpton, H. Fujyosh, and R. Patl, Movng Target Detecton and Classfcaton from Real-tme Vdeo, WACV, 998. Page 6

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