Robust Realtime Face Recognition And Tracking System

Transcription

1 JCS& Vol. 9 No. October 9 Robust Realtme Face Recogto Ad rackg System Ka Che,Le Ju Zhao East Cha Uversty of Scece ad echology Emal:asa85@hotmal.com Abstract here s some very mportat meag the study of realtme face recogto ad trackg system for the vdeo motorg ad artfcal vso. he curret method s stll very susceptble to the llumato codto, o-real tme ad very commo to fal to track the target face especally whe partly covered or movg fast. I ths paper, we propose to use Boosted Cascade combed wth sk model for face detecto ad the order to recogze the caddate faces,they wll be aalyzed by the hybrd Wavelet, PCA(prcple compoet aalyss) ad SVM(support vector mache) method. After that, Meashft ad Kalma flter wll be voked to track the face. he expermetal results show that the algorthm has qute good performace terms of real-tme ad accuracy. Keywords;PCA; meashft; Kalma flter; svm; wavelet; Realtme face detecto; Realtme face trackg; face recogto I. INRODUCION Over the last te years, face recogto ad object trackg techology have become a cetral problem eural etwork as well as statstcs ad sgal processg. It s a relatvely dffcult sythetc problem to mplemet the face recogto ad trackg together, sce t requres the syergetc effort of mache learg, patter recogto, mache vso ad mage processg. Face s oe of the most popular bologcal features the curret research, whch have profoud meag research ad a great potetal applcato such as safety supervsg, vdeo meetg ad huma-computer teracto. he system should be able to detect, recogze ad track the face realtme. he curret methods has several problems lke false retrval of faces, the fulece of mage ose, the low accurate rate of face recogto, the lack of real tme ad lose track of the target face. I ths paper, we propose a adaptve threshold face detecto method whch combes the Boosted Cascade wth the sk model. After that, face caddate rego wll be aalyzed by the algorthm based o the combato of dscrete dgtal wavelet, prcple compoet aalyze ad SVM. Accordg to the smlarty of the caddate face, the target wll be tracked by the algorthm based o the tegrato of meashft ad Kalma flter. he result of the expermet dcates that the proposed method produces a sgfcat mprovemet. he rest of the paper s orgazed as follows. Secto provdes the detal boosted cascade wth adaptve threshold ad sk model for face detecto. Secto 3 desrbes the algorthm composed of DW,PCA ad SVM for face recogto. Secto 4 troduces the meashft ad kalma flter for trackg face. he Expermetal results are preseted Secto 5. Setco 6 gves the cocluso. II. BOOSED CASCADE WIH ADAPIVE HRESHOLD AND SKIN MODEL FOR FACE DEECION he Boosted Cascade stll have the lmtato of false face retrval, spte of the fact that t ca acheve hgh performace face detecto. I order to address ths problem, We propose a method whch the mage s processed by sk model to get the face color rego, the the face detectg wdow wll skp the rego where the rate of sk color s less tha percet, whe t s scaed across the mage at multple scales ad locato. o acheve hgher accurate rate, the threshold of boosted cascade s versely propotoal to the sk color rate 8

2 JCS& Vol. 9 No. October 9 so the threshold wll be adaptve based o the rate. Although a lttle bt more expesve computatoal term, the false postve rate ad resdual error rate ca be cut dow dramatcally. Fgure. Face Detecto FrameWork Boosted Cascade s a learg-based approach ad composed of serveral weak classfers whch ca dscrmate face ad o-face o the bass of Haar-lke flters [6] (lke fgure ). hese flters cosst of two or three rectagles. o compute the feature value, the sum of all the pxels values grey rego are substracted from the sum of all the pxels values whte rego wth the tegral mage, So the faces ca be preseted by these rectagle flters dfferet scale. I ths case, the power of classfcato system ca be boosted from several weak classfers based o smple, local,haar-lke features. Fgure. set of Haar-lke feature he Boosted Cascade ca acheve relatve hgh perfomace real-tme ad accuracy. However, o-face ca ot be elmated completely. Oe way to get rd of o-face rego s va the use of the sk model whch are qute effectve at dscrmatg sk ad o-sk rego[7], so the o-face rego wth the low rate of the color wth the rage of sk color ca be elmated. o get the sk color rego, the RGB space are trasformed to Ycbcr ad the Y s removed to reduce the fluece of llumace, the sese that the sk value s relatvely cetralzed two dmesoal space. By solvg the dstace betwee the pxel value ad the ceter of gaussa of sk color space, the smlarty of pxel value ca be computed as P( r, b) exp[.5( x m) C ( x m)], () where m s the mea value, ad C s the varace of the sk color space. After that, the threshold s defed by OSU to get the bary mage. Aother obvous defect of boosted cascade s that the threshold the strog classfer s fxed so that t s very hard to atta a hgh correct detecto rate whle obtag very low false postve rate. o ths ed, the adaptve threshold of strog classfer s set based o the rate of sk color wth the detectg wdow. Fgure 3. Left: orgal mage. Mddle:mage based o smlarty.rght:barymage By combg the sk model wth Boosted Cascade, there s a great mprovemet the correct detecto rate. I the ext secto, the caddate face wll be aalyzed by the hybrd algorthm wth wavelet, PCA ad SVM. III. RECOGNIZING FACE WIH WAVELE, PCA AND SVM I most cases, Face recogto s a two-step process of subspace projecto followed by classfcato. I ths paper, we ehace the face matchg techque, for example by pre-processg the mage wth wavelet ad substtue the earest eghbor classfer wth SVM. As the computatoal testy of PCA crease sharply wth the put sze, t wll cost too much computato the stage of trag ad recogto,especally whe the sze of face mage s qute large. As a result, t wo t be fast eough to aalyze the face mage realtme. I order to address ths problem, we propose to utlze level dscrete wavelet trasform to obta the subbad represetato of the face data by processg face mage wth w ( t) hkw ( t k) k Z w+ ( t) hkw ( t k) k Z whch hk s the db low flter ad hk () s the 83

3 JCS& Vol. 9 No. October 9 db hgh flter because there s some fast wavelet algorthm avalable [9],the the approxmato coeffcets wll be saved,whle the detal coeffcets s dscarded. I ths way, t ca decrease the tme of processg ad the storage space of PCA trag data sgfcatly uder the premse that the recogto rate s t reduced, whe dealg wth huge amout of trag faces. After pre-processg the face mage, PCA wll be used to extract the orthogoal bass vectors ad the correspodg egevalue from the set of trag face mages. PCA s oe of the most popular method to face recogto by projectg data form a hgh-dmesoal space to a low-dmesoal space. I 987 Srovch ad Krby used Prcpal Compoet Aalyss (PCA) order to obta a reduced represetato of face mages [3]. I essece, PCA s a optmal compresso scheme that mmze the mea square error betwee the orgal mages ad ther recostructos[4,5]. o perform PCA, each of the trag mages should be the same A [ sze. Let s deote... M ] as the trag set of faces whch each colum represets a face mage M ad the substract the mea face M from each colum M M X (3) After that, extract the egevector(egeface) ad the egevalue from the covarace matrx Because of C XX. the computatoally tesve covarace matrx, sgular value decomposto s used X UEV, where U cotas the egevectors of the covarace matrx C ad the each trag mage wll be projected to the egespace as a weght Y U X,,..., M.I ths [ Y way, the feature value Y M ] of the trag faces ca be obtaed. o fd the closest match ad mprove the recogto rate further, SVM s used as the classfer stead of eural etwork []. I order to establsh the optmal SVM classfer, each stace of the face feature the trag set should cotas oe class label ad several feature values. Whe beg tested, SVM model wll predct the class label of the test-face whch are gve oly the feature values. Suppose we are gve a trag set of stace-label pars ( x, y ),,..., l where x R ad y {, } l, to fd a lear separatg hyperplae wth the maxmal marg ths hgher dmesoal space, the followg optmzato problem l m( w w+ C ξ ) wb,, ξ should be resolved whch s subject to (4) y ( w φ( x ) + b) ξ, ξ. (5) I our method, we take the radal bass fucto(rbf): (, j ) exp( r x xj ), r> DW PCA K x x (6) as the kerel fucto, sce ulke the lear kerel t ca hadel the case whe the relato betwee class labels ad feature values s olear, the use cross-valdato to fd the best parameter C ad γ for prevetg the overfttg problem. After pre-processg, feature extracto ad classfcato, the target face wll be tracked by the algorthm supported by mea-shft ad kalma flter. IV. Fgure 4. face matchg framework RACKING FACE WIH MEAN-SHIF AND KALMAN FILERING Whe the complex backgroud, mea shft wll fal to track the target face caused by partly covered or fast movemet, as meashft tracks the face oly by the face color wthout movemet predcto [], t s very lkely to lose track of the target face. O the purpose of mprovg robustess, the two dmesoal Kalma flter s utlzed to predct the movemet ad posto of the movg target. SVM trag As a appearace based trackg method, the 84

4 JCS& Vol. 9 No. October 9 meashft trackg algorthm updates the weght of each pxels the rego ad employs the meashft teratos to fd the caddate target whch s the most smlar to a gve model terms of testy dstrbuto[3], wth the smlarty of the two dstrbutos beg expressed by a metrc based o the Bhattacharyya coeffcet []. Gve the posto of the ceter of target face s y, ad the caddate { x } posto mght be, x,..., x caddate desty could be y x k pu ( y) Ch k( ) h ad the target desty s * ( ) qu C k x C where the k( x ) kerel fucto. Wth *, So the target ad h s the badwth of q u ad coeffcet ca be represeted as ρ( y) ρ[ p( y), q] m u p ( y) u q u ad the smlarty dstace ca be d( y) (7) (8) p u, Bhattacharyya (9) ρ[ p( y), q ]. By traslatg the wdow the drecto of meashft vector, the target could be tracked by the algorthm, but t stll have some lmtatos the trackg. Fgure 5. the weght of each pxels the meashft trackg rego I order to mprove the robustess, two dmetoal Kalma flter ca be used to predct the startg posto for mea shft terato the k+ frame o the bass of the ceter posto of the target A the k frame. Let s deote ( t s terval betwee frames) as the state trasto matrx t t ad wk ( ) as system perturbato, v( k ) be the ose, H be the observablty matrx. herefore, the state of the system ca be modeled as x ( k) A* x( k ) + w( k) () ad the correspodg observato equato s y ( k) H * x( k) + v( k), () whch tme k ad x ] ( k) [ x, y, dx, dy s the state vector at y ] ( k) [ x, y s the observed value, So the state vector xk+ ( ) ca be updated usg the system model ad measuremet model. Whe the object moves aroud, the sze of the trackg wdow should be adaptve o the purpose of mprovg the trackg stablty. LOG flter ca be used to deal wth ths problem. As metoed [6], we substtute the LOG flter wth DOG flter to reduce the processg tme. DOG( x; σ ) G( x; σ ) G( x;.6α ) () Fgure 6 left::remote rage face Rght::ear rage face he above mages are the result of DOG flter. Whe the face approaches the camera, the sum of the gray scale the trackg blob wll crease ad vce versa. hereby we ca chage the szeof trackg blob based o the chage rate of the gral scale of the DOG result. I ths way the trackg wdow ca adjust ts sze, whe the face approaches or moves away. V. EXPERIMENAL RESULS All the expermets are executed o a computer wth.73ghz petum M processor ad 5Mb ram the wdows xp system; besdes, all the algorthm program s mplemeted c++ code wth VC 6.. Face detecto expermet We use MI CBCL face databases as the face detectg trag set whch cossts of 49 faces 85

5 JCS& Vol. 9 No. October 9 ad 4548 o-faces 9 by 9 pxels grayscale mages. Fgure 7 trag face ad two features of weak classfer. As sk detectg s added to the face detecto, t wll take more tme to detect the faces compared wth orgal adaboost algorthm. O the purpose of reducg the tme for detectg sk, the sk mage s coverted to a tegral mage. By dog ths, the detectg tme for processg a 3 by 4 pxel mage ca be reduced from 35ms to 87ms. Although detectg tme s lttle bt loger tha that of adaboost detector, there s a great mprovemet the correct detecto rate. x xj K( x, xj ) exp( ) σ whle σ. 5, C.35. Four face mages (3) each face mage set are selected to compose the face trag set ad the left sx mages of each perso are utlzed as the testg set. he result of expermet s lsted table ad fgure.he tme requred for recogzg each face cost about 85ms. ABLE : HE ACCURAE RAE OF WO ALGORIHM UNDER DIFFEREN NUMBER OF SAMPLES IN EACH CLASS. he umber of samples each class Wavelet+PCA+SVM 96.% 96.% 9.% 87.% Fsher 9.% 9.% 87.% 8% PCA 88.% 87.% 85.% 78.% Fgure 8. output of adaboost Fgure. the accurate rate uder dfferet umber of Fgure 9. output of out detector Face recogto expermet we use the ORL face databases, wth whch there are 4 face mages( 9) of 4 people wth dfferet gesture ad expresso uder dfferet llumato codto. feature vectors Face rackg expermet As for trackg face, the followg pctures dcate the trackg performace realtme whe movg fast ad partly covered. he tme for trackg target face each frame s about 3ms wth 34 percet of full cpu load. Fgure. ORL face mages We pre-process the mage wth the wavelet flter db ad the extract 65 feature values each mage. I the SVM, as dscussed secto 3,RBF s adopted as the kerel fucto. Fgure. rackg face partly covered 86

6 JCS& Vol. 9 No. October 9 Fgure 5. Program flowchart Fgure 6. the output of the our program Fgure 3. rackg movg face by oly meashft Fgure 4. rackg movg face by meashft ad kalma flter Compared wth the covetoal meashft algorthm, we ca coclude that our algorthm s much more robust especally whe the face s movg fast ad partly covered. Camera DrectShow terface VI. CONCLUSIONS I ths paper, we propose a realtme face recogzg ad trackg system whch ca detect face, the recogze ad track t. Our method performs well regardless of whether the faces s the complex backgroud, movg fast or partly covered, ad the hybrd algorthm Wavelet/PCA/SVM further ehace the accuracy of face recogto. By expermetal results, we have demostrated that the proposed method dramatcally mproves the robustess ad accuracy of the real-tme face recogzg ad trackg system REFERENCES [] D. Comacu, V. Ramesh.. Mea Shft ad Optmal Predcto for Effcet Object rackg, IEEE It l Cof. Image Processg, Vacouver, Caada, Vol. 3: [] D. Comacu, V. Ramesh..Robust Detecto ad rackg of Huma Faces wth a Actve Camera, IEEE It l Workshop o Vsual Survellace, Dubl, Irelad, -8. N Detect face every ms Get face Face matchg Fd arget Get target from database rackg target face N [3] D. Comacu, V. Ramesh, P. Meer.. Real-me rackg of No-Rgd Objects usg Mea Shft, o appear, IEEE Cof. o Comp. Vs. ad Pat. Rec., Hlto Head Islad, South Carola. [4] Che J, Wu C C.. Dscrmat waveletfaces ad earest feature classfers for face recogto [J]. IEEE rasactos o Patter Aalyss ad Mache Itellgece, 4(): [5] Yag Mghsua, Kregma D J, Ahuja N.. Detectg faces mages-a survey. IEEE rasactos o Patter Aalyss ad Mache Itellgece, 4(): [6] Vola P, Joes M. 4. Robust Real-me Face Detecto[J] Iteratoal Joural of ComputerVso 57():

7 JCS& Vol. 9 No. October 9 [7] Douglas Nga Kg N. Face Segmetato Usg Sk-color Map Vdeophoe Applcatos[J]. IEEE ras o CS 9(4): [8] C.W.Hsu ad C.J.L.. A smple decomposto method for support vector maches.mache Learg, 46: [9] Huluta, E.; Petru, E.M.; Das, S.R.; Al-Dhaher, A.H.;Istrumetato ad Measuremet echology Coferece,. IMC/. Proceedgs of the 9th IEEE Volume, -3 May Page(s): vol. [] S.J.McKea, Y.Raja, S.Gog rackg Colour Objects usg Adaptve Mxture Model, Image ad Vso Computg,7: 3-9. [] G. Da ad C. L. Zhou, Face Recogto Usg Support Vector Maches wth the Robust Feature, Proc. of the 3 IEEE Iteratoal Workshop o Robot ad Huma teractve Commucato, 3. [] Vola P, Mchael J. Rapd Object Detecto Usg a Boosted Cascade of Smple Features[C]. Proc. of IEEE Cof. o Computer Vso ad Patter Recogto, Kaua, Hawa, USA.. [3] Srovch L., ad Krby M A low-dmesoal procedure for the characterzato of huma faces, J.Opt. Soc. Amer. A, vol.4, o. 3, pp [4] P. Belhumeur, J. Hespaha, D. Kregma Egefaces vs. Fsherfaces: Recogto usg class specfc lear projecto. IEEE rasactos o Patter Aalyss ad Mache Itellgece, 9(7):7 7 [5] M. Krby, L. Srovch. 99. Applcato of the Karhue-Loeve Procedure ad the Characterzato of Huma Faces. IEEE rasactos o Patter Aalyss ad Mache Itellgece, ():3 8. [6] R..Colls. 3. Meashft Blob rackg through Scale Space. IEEE Coferece o Computer Vso ad Patter Recogto, Vol., pp