Robust Multi-view Face Detection Using Error Correcting Output Codes

Transcription

1 Robus Muli-view Face Deecion Using Error Correcing Oupu Codes Hongming Zhang,2, Wen GaoP P, Xilin Chen 2, Shiguang Shan 2, and Debin Zhao Deparmen of Compuer Science and Engineering, Harbin Insiue of Technolog Harbin, 5000, China 2 Insiue of Compuing Technolog Chinese Academy of Sciences, Beijing, 00080, China {hmzhang, wgao, xlchen, sgshan, dbzhao}@jdl.ac.cn Absrac. This paper presens a novel mehod o solve muli-view face deecion problem by Error Correcing Oupu Codes (ECOC). The moivaion is ha face paerns can be divided ino separaed classes across views, and ECOC muli-class mehod can improve he robusness of muli-view face deecion compared wih he view-based mehods because of is inheren error-oleran abiliy. One key issue wih ECOC-based muli-class classifier is how o consruc effecive binary classifiers. Besides applying ECOC o muli-view face deecion, his paper emphasizes on designing efficien binary classifiers by learning informaive feaures hrough minimizing he error rae of he ensemble ECOC muli-class classifier. Aiming a designing efficien binary classifiers, we employ spaial hisograms as he represenaion, which provide an overcomplee se of opional feaures ha can be efficienly compued from he original images. In addiion, he binary classifier is consruced as a coarse o fine procedure using fas hisogram maching followed by accurae Suppor Vecor Machine (SVM). The experimenal resuls show ha he proposed mehod is robus o muli-view faces, and achieves performance comparable o ha of sae-of-he-ar approaches o muli-view face deecion. Inroducion Auomaic deecion of human faces is significan in applicaions, such as humancompuer ineracion, face recogniion, expression recogniion and conen-based image rerieval. Face deecion is a challenge due o variabiliy in orienaions, parial occlusions, and lighing condiions. A comprehensive survey on face deecion can be found in []. Many approaches have been proposed for face deecion, hese approaches can be classified as wo caegories: global appearance-based echnique and componen-based echnique. The firs one assumes ha a face can be represened as a whole uni. Several saisical learning mechanisms are explored o characerize face paerns, such as neural nework [2,3], probabilisic disribuion [4], suppor vecor machines [5,6], naive Bayes classifier [7], and boosing algorihms [8,9]. The second mehod reas a face as a collecion of componens. Imporan facial feaures (eyes, nose and mou are firs exraced, and by using heir locaions and relaionships, he faces are deeced [0]. A. Leonardis, H. Bischof, and A. Pinz (Eds.): ECCV 2006, Par IV, LNCS 3954, pp. 2, Springer-Verlag Berlin Heidelberg 2006

2 2 H. Zhang e al. So far here are hree ways for muli-view face deecion. The firs scheme is a view-based approach. In he raining sage, separae face deecors are buil for differen views. In he esing sage, all hese deecors are applied o he image and heir resuls are merged ino final deecion resuls [4,7,9]. [] uses a pose esimaor o selec a deecor o find faces of he chosen view. The second scheme is described in [2] for roaed-face deecion, which calculaes he in-plane roaion angle of inpu image, and roaes he inpu image for a fronal face deecor. The hird way is o approximae smooh funcions of face paerns across various views [3] or face manifold parameerized by facial pose [4]. Moivaed by he idea ha face paerns can be naurally divided ino disinc classes according o separaed facial poses, his paper proposes a novel mehod ha deecs muli-view faces using a muliclass classifier based on error correcing oupu codes (ECOC). Wih is inheren error-oleran proper ECOC can improve he robusness o pose variaion for face deecion. Dieerich and Bakiri [5,6] presened he idea of reducing muliclass problems o muliple binary problems based on ECOC. ECOC classifier design concep has been used in many applicaions, such as ex classificaion [7] and face verificaion [8]. In ECOC relaed applicaions, one key issue is he problem how o consruc opimal binary classifiers for an effecive ECOC muli-class classifier. In [9], an approach is presened o learn good discriminaor in linear feaure space for objec recogniion. In he proposed mehod, we emphasize on designing efficien binary classifiers by learning informaive feaures hrough minimizing he error rae of he ensemble ECOC muli-class classifier. Aiming a designing efficien binary classifiers, we propose o use spaial hisogram feaures as represenaion and use hierarchical classifiers ha combine hisogram maching and suppor vecor machine (SVM) as binary classifiers. Secion 2 briefly describes he background of ECOC-based muli-class classificaion mehod. The overview of he proposed ECOC-based muli-view face deecion approach is given in Secion 3. Face represenaion used in he proposed mehod is described in Secion 4. In Secion 5, he mehod of learning an ECOC-based muliview face deecor hrough minimizing error rae is presened. Experimenal resuls are provided in Secion 6. Conclusions are given in Secion 7. 2 Background of ECOC-Based Muli-class Classificaion Le S = {( x, y ),...,( x m, y m )} be a se of m raining samples where each insance x i belongs o a domain X, and each label y i akes values from a discree se of classes Y = {,..., k}. The ask of learning a muliclass classifier is o find a funcion H : X Y ha maps an insance x ino a class label y, y Y. To undersand he mehod for solving muliclass learning problems via ECOC, consider a {0,}-valued marix Z of size k n where k is he number of classes and n is he lengh of he unique binary sring assigned o each class as is code word. The k rows are well separaed wih large Hamming disance beween any pair. For each column, he insances are relabeled as wo super classes according o he binary vales (s and 0s).

3 Robus Muli-view Face Deecion Using Error Correcing Oupu Codes 3 The muliclass learning mehod consiss of wo sages. () In he raining sage, a se of n binary classifiers is consruced, where each classifier is o disinguish beween he wo super classes for each column. These binary classifiers are called base classifiers. (2) In he esing sage, each insance is esed by he base classifiers, and is represened by an oupu vecor of lengh n. The disance beween he oupu vecor and he code word of each class is used o deermine he class label of he insance. 3 Overview of he Proposed ECOC-Based Face Deecion Mehod We divide face paerns ino hree caegories: fronal faces, lef profile faces, righ profile faces, according o facial pose variaion ou of plane. Adding non-face paerns ogeher, we have four classes o be recognized in oal. Therefore, we forma muliview face deecion as a muli-class problem wih four classes, and explore he problem of learning ECOC-based classifier for muli-view face deecion. Since k = 4, we consruc a complee code of lengh n = 7, as shown in Table. No columns or no rows are idenical or complemenary in he code. For each column, one base classifier is needed o idenify he super classes (refer o Secion 2). In oal, seven base classifiers { b0, b,..., b6} are o be consruced o form an ensemble classifier. According o informaion heor his code has error correcing abiliy for any base classifier. Table. ECOC codes for face deecion b 0 b b 2 b 3 b 4 b 5 b 6 Non face paern (CB0B) Fron face paern (CBB) Lef profile face paern (CB2B) Righ profile face paern (CB3B) We uilize an exhausive search sraegy o deec muliple faces of differen sizes a differen locaions in an inpu image. The process of objec deecion in images is summarized in Fig.. I conains hree seps: image sub sampling, objec classificaion and deecion resuls fusion. In he Sep, he original image is repeaedly reduced in size by a facor.2, resuling in a pyramid of images. A small window (image window) wih a cerain size 32x32 is used o scan he pyramid of images. Afer a sub image window is exraced from a paricular locaion and scale of he inpu image pyramid, i is fed o he following procedures in he Sep 2. Firsl spaial hisogram feaures are generaed from his image window. Secondl an ECOC-based muli-view face paern classifier is used o idenify wheher he sub window conains a muli-view face. The Sep 3 is a sage for deecion resuls fusion. Overlapped face insances of differen scales are merged ino final deecion resuls. In he sep 2, he inpu o he muli-view face deecor is a vecor x, which is consiued by spaial hisogram feaures (refer o Secion 4 for deails) obained on he

4 4 H. Zhang e al. Sep : Image pyramid consrucion Sep 2: Muli-view face paern classificaion Sep 3: Deecion resuls fusion Image window Exrac spaial hisogram feaures ECOC_based muli-view face deecor Oupu deecion resul Inpu image ECOC_based muli-view face deecor Spaial Hisogram Feaures Se 0 Hisogram Maching SVM Classificaion Base Classifier ( b 0 ) v 0 x Spaial hisogram feaures vecor Spaial Hisogram Feaures Se 6 Hisogram Maching SVM Classificaion Base Classifier ( b 6 ) v 6 ECOC Decoder H(x) Fig.. The process of muli-view face deecion in images image window. For each base classifier, specific spaial hisogram feaures are used as inpu. Hisogram maching and SVM classificaion are performed hierarchically o idenify which super class he vecor belongs o (refer o Secion 5 for deails). The binary oupus by he base classifiers is ransformed ino an {0,}-oupu vecor of lengh n = 7, given as V = v, v,..., ], () [ 0 v6 where v j is he oupu of j h classifier, j = 0,,..., 6. The disance beween he oupu vecor and he code word of each class is deermined by Hamming disance: 6 Lc i = Zij v j,( i = 0,...,3). (2) j= 0 The es insance is assigned o he class label whose code word has minimum disance, by he ECOC decode rule given by H ( x) = arg min{ L i = 0,,...,3}. (3) c i c i 4 Spaial Hisogram Feaures for Face Represenaion For each column, we refer he super class labeled by s as objec, and labeled by 0s as non-objec. Similar o our previous work [2], spaial hisogram feaures in are used for objec represenaion, as illusraed in Fig. 2. Spaial emplaes are used o encode spaial disribuion of paerns. Each emplae is a binary recangle mask and is

5 Robus Muli-view Face Deecion Using Error Correcing Oupu Codes 5 denoed as r (, where ( x, y) is he locaion and ( is he size of he mask respecively. We model he sub image wihin he masked window by hisogram. This kind of hisograms is called as spaial hisograms. For a sample P, is spaial hisogram associaed wih emplae r ( is denoed as SH ( P) r(. Fig. 2. Objec spaial disribuion is encoded by spaial hisograms Suppose a daabase wih n objec samples and a spaial emplae, we represen objec hisogram model over he spaial emplae by he average spaial hisogram of he objec raining samples, defined as: SH r( = n n j= SH r( ( P ), (4) where P j is an objec raining sample, and r ( is he spaial emplae. For any sample P, we define is spaial hisogram feaure as is disance o he average objec hisogram, given by r( r( r( f ( P) = D( SH ( P), SH ), (5) where D ( H, H 2 ) is he similariy of wo hisograms measured by inersecion [20]. An objec paern is encoded by m spaial emplaes. Therefore, an objec sample is represened by a spaial hisogram feaure vecor in he feaure space: r( ) r( m) j F = [ f,..., f ]. (6) Feaure discriminaing abiliy: For any spaial hisogram feaure f j ( j m ), is discriminaive abiliy is measured by Fisher crierion where S b is he beween-class scaer, and Sb J ( f j ) =, (7) S w S w is he oal wihin-class scaer. Feaures correlaion measuremen: Given wo spaial hisogram feaures f and f 2, we calculae he correlaion beween wo feaures f and f 2 as

6 6 H. Zhang e al. I( f f 2 ) Corr ( f, f 2 ) =, H ( f ) (8) where H ( f ) is enropy of f, I ( f 2 ) f is he muual informaion of f and f 2. Le Fs be a feaure se, he correlaion beween F s and a feaure f Fs is given by Corr f, F ) = max{ Corr( f, f ) f F }. (9) ( s k k s 5 Learning ECOC-Based Classifier for Muli-view Face Deecion We apply a hierarchical classificaion using cascade hisogram maching and SVM as base classifier o objec deecion. In his secion, we presen he mehod of designing efficien binary classifiers by learning informaive feaures hrough minimizing he error rae of he ensemble ECOC muli-class classifier. 5. Cascade Hisogram Maching Hisogram maching is a direc mehod for objec recogniion. Suppose P is a sample r( and is spaial hisogram feaure wih one emplae r( is f ( P), P r( is classified as objec paern if f ( P) θ, oherwise P is classified as nonobjec paern.θ is he hreshold for classificaion. We selec mos informaive spaial hisogram feaures and combine hem in a cascade form o perform hisogram maching. We call his classificaion mehod as cascade hisogram maching. If n spaial hisogram feaures f,..., f n wih associaed classificaion hresholds θ,...,θ n are seleced, he decision rule of cascade hisogram maching is as follows: objec if ( )... ( ) ( ) = f P θ f n P θ n CH P (0) 0 non - objec oherwise For each column, suppose ha we have () spaial hisogram feaures space F = { f,..., f m }, (2) posiive and negaive raining ses: SP and SN, (3) a posiive validaion se VP = {( x, y ),...,( x n, y n )}, and a negaive validaion se ' ' ' ' VN = {( y),...,( x k, yk )}, where x i and x i ' are samples wih m dimensional spaial ' hisogram feaure vecors, y i = and y i = 0, (4) accepable deecion rae: D. The mehod for raining cascade hisogram maching is given in he following procedure:. Iniializaion: F selec =, ThreSe =, = 0, Acc ( pre) = 0, Acc ( cur) = 0; 2. Compue Fisher crierion J ( f ) using SP and SN, for each feaure f F ; 3. Find he spaial hisogram feaure f which has he maximal Fisher crierion value, f = arg max{ J ( f ) f F} ; f j j j

7 Robus Muli-view Face Deecion Using Error Correcing Oupu Codes 7 4. Perform hisogram maching wih f on he validaion se V = VP VN, find a hreshold θ such ha he deecion rae d on he posiive validaion se VP is greaer han D, i.e., d D ; 5. Compue he classificaion accuracy on he negaive validaion se VN, k ' ' Acc( cur) = CH ( x i ) y i. CH (x) is he oupu by hisogram maching k i= wih f and θ, CH ( x) {0, } ; 6. If he classificaion accuracy saisfies condiion: Acc ( cur) Acc( pre) ε ( ε is a small posiive consan), he procedure exis and reurns F selec and ThreSe, oherwise process following seps: (a) Acc ( pre) = Acc( cur), SN =, F selec = F selec { f }, F = F \ { f }, ThreSe = ThreSe { θ }, = +, (b) Perform cascade hisogram maching wih F selec and ThreSe on an image se conaining no arge objecs, pu false deecions ino SN, (c) Go o sep 2 and coninue nex ieraion sep. 5.2 Consrucion of he ECOC-Based Muli-view Face Deecor Cascade hisogram maching is he coarse objec deecion sage. To improve deecion performance, we employ SVM classificaion [22] as fine deecion sage. By minimizing error rae, we consruc an ECOC-based muli-view face deecor. Suppose ha we have () a spaial hisogram feaures space F = { f,..., f m }, (2) a raining se s = {( x, y ),...,( x n, y n )} and a esing se {( ', ' ),...,( ', ' v = x y x k y )}, k where x i and x i ' are samples wih m dimensional spaial hisogram feaure vecors, ' yi {0,,2,3} and y i {0,,2,3 }, (3) ECOC code marix Z of size k n, ( k = 4, n = 7) as lised in Table. The consrucion of he ECOC-based muli-view face deecor is performed as he following procedure:. Using he mehod for raining cascade hisogram maching (see secion 5.), consruc a cascade hisogram maching classifier as base classifier for each column. These base classifiers { b0,..., b6} consiue he ECOC muli-class classifier; 2. Se classificaion accuracy Acc ( pre) = 0 ; for each column, find f m i wih maximum Fisher crierion, { m i i i i F selec = f } and F ori = F \ { f m}, i = 0,,..., 6; 3. Compue each base classifier's error rae; find he base classifier b ( 0 6), which has maximum error rae, and updae he base classifier as follows: (a) Compue Fisher crierion J ( f ) and feaure correlaion Corr( f, F selec ) on he raining sample se, for each feaure f F ori ;

8 8 H. Zhang e al. (b) Compue Thre as follows: MinCorr = min{ Corr( f, Fselec ) f Fori} MaxCorr = max{ Corr( f, Fselec ) f Fori}, Thre = MinCorr * ( α) + MaxCorr * α here α is a balance weigh ( 0 < α < ), we choose α = 0. 2 in experimens; (c) Find f ' F ori wih large Fisher crierion as below: f ' = arg max( J ( f ) Corr( f, F ) Thre) ; f j i= j (d) Train a SVM classifier C on he raining se s, using f ' and F selec ; updae b wih cascade hisogram maching and he SVM classifier C ; updae he ECOC muli-class classifier wih b ; 4. Evaluae he ECOC muli-class classifier on he esing samples se v, and compue he classificaion accuracy: k ' ' x y Acc( cur) = S( C( x i ), y i ), S( y) =. k 0 x = y i selec Here, C (x) is he classificaion oupu by he classifier C, C ( x) {0,,2,3 }; 5. If he classificaion accuracy saisfies condiion: Acc ( cur) Acc( pre) ε ( ε is a small posiive consan), process following seps: (a) Acc ( pre) = Acc( cur), Fselec = Fselec { f '}, Fori = Fori \ { f '}, (b) Go o sep 3 and coninue nex ieraion sep. 6. The procedure exis and reurns he ECOC muli-class classifier, which is consiued by bi and F ( i = 0,,..., 6). 6 Experimenal Resuls We implemen he proposed approach and conduc experimens o evaluae is effeciveness. Our raining sample se consiss of,400 fronal face images, 4,260 lef profile face images, 4,080 righ profile face images, and 7,285 non-face images, each of sandard size 32x32. The exhausive spaial emplae se wihin 32x32 image window is 832,35, a very large amoun. Afer reducing redundanc 80 spaial emplaes are evaluaed o exrac spaial hisogram feaures. For each base classifier, abou 9~5 spaial emplaes are learned for cascade hisogram maching and 20~25 are learned for SVM classificaion wih RBF kernel funcion in our experimen. The muli-view face deecor is composed of hese base classifiers. Experimen : Error-Toleran Performance Evaluaion In order o evaluae he error-oleran performance of he ECOC-based muli-view face deecor, we collec anoher sample se for esing. This se conains 5,400 fronal face images, 3,0 lef profile face images, 3,546 righ profile face images, and 6,527 non-face images, each of sandard size 32x32. j selec

9 Robus Muli-view Face Deecion Using Error Correcing Oupu Codes 9 In Table 2, classificaion error raes of binary classifiers in he ECOC-based muliview face deecor are presened. Table 3 shows classificaion error raes of he ECOC-based muli-view face paern classifier. The error raes are decreased afer using ECOC o combine all he base classifiers. These resuls demonsrae ha he sysem has error-oleran abiliy and i is be able o recover from he errors of single base classifier. Table 2. Classificaion error raes of he base classifiers b 0 b b 2 b 3 b 4 b 5 b 6 Error rae 8.4% 8.4% 8.3% 7.9% 4.7% 25.0% 22.9% Table 3. Classificaion error raes of he ECOC-based muli-view face paern classifier Class Number of esing samples Error rae Non face paern (CB0B) % Fron face paern (CBB) % Lef profile face paern (CB2B) % Righ profile face paern (CB3B) % Toal % Experimen 2: Tesing Resuls on Sandard Daa Ses We es our sysem on wo sandard daa ses. One is MIT+CMU se [2,4], which conains 30 images wih 507 fronal faces. The oher is CMU-PROFILE [7], which consiss of 208 images wih 44 faces from full fronal view o side view. Abou 347 faces are in profile pose. The ROC curves are shown in Fig. 3. In Fig. 4, some face deecion examples are given. The examples demonsrae ha our approach can handle muliple faces wih complex backgrounds. Comparison resuls are shown in Table 4 and Table 5. Our sysem exhibis superior performance han [2,9,,4] wih higher deecion rae, and achieves comparable performance compared wih he sysem of [7,8]. Deecion Rae (%) Deecion Rae (%) False Posiive Rae (a) -5 P x0p False -5 P Posiive Rae x0p Fig. 3. ROC curves of face deecion on (a) CMU+MIT es se, (b) CMU-PROFILE es se (b)

10 0 H. Zhang e al. Fig. 4. Some examples of muli-view face deecion Table 4. Face deecion raes on MIT+CMU se False alarms Jones and Viola [8](fronal) 85.2% 92.0% 93.9% Rowley e.al [2] 85.0% N/A 90.% Schneiderman and Kanade [7] N/A 94.4% N/A Li and Zhang [9] 89.2% N/A N/A Our approach 90.7% 92.3% 94.2% Table 5. Face deecion raes on CMU-PROFILE se False alarms Jones and Viola [](profile) 70% 83% Schneiderman and Kanade [7] 86% 93% Osadch Miller, LeCun [4] 67% 83% Our approach 82% 90% Experimen 3: Performance Comparison wih One-Agains-Ohers Codes We also conduc experimens o compare performance of ECOC codes wih ha of one-agains-ohers codes. Table 6 gives he one-agains-ohers code marix for muliview face deecion. In each column, a binary classifier is consruced for each face Table 6. One-agains-ohers code for face deecion bb0b bbb bb2b Non face paern (CB0B) Fron face paern (CBB) 0 0 Lef profile face paern (CB2B) 0 0 Righ profile face paern (CB3B) 0 0

11 P P Robus Muli-view Face Deecion Using Error Correcing Oupu Codes Deecion Rae (%) False Posiive Rae Fig. 5. Face deecion performance comparison beween ECOC wih one-agains-oher: ROC on CMU-PROFILE es se x0-5 class agains oher face classes and non-face class. This code has no error correcing abiliy for base classifiers. Fig. 5 shows he ROC comparison beween he sysem using ECOC codes and he sysem using one-agains-ohers codes. The comparison resul shows ha ECOCbased sysem achieves superior performance wih higher deecion raes. 7 Conclusions In his paper, we solve muli-view face deecion problem by using ECOC. The key issue is how o rain effecive binary classifiers for an efficien ECOC-based muliview face deecor. Our mehod consrucs binary classifiers by learning informaive feaures hrough minimizing he error rae. For purpose o obain efficien binary classifiers, our mehod employs spaial hisogram feaures as represenaion and hierarchical classifiers as binary classifiers. Exensive experimens show ha ECOC improves he robusness o pose variaion for face deecion, and he proposed approach is efficien in deecing muli-view faces simulaneously. Tess on sandard daa ses show ha he proposed mehod achieves performance comparable o ha of sae-ofhe-ar approaches o muli-view face deecion. The proposed approach of consrucing ECOC-based muli-classifier by learning base classifiers can be viewed as a general framework of muli-classes problem based on a given code marix. In he fuure work, we plan o apply his approach in muliclass objecs deecion wih more kinds of objecs. Acknowledgemens This research is suppored by Naional Naure Science Foundaion of China ( ), 00 Talens Program of CAS, China, he Program for New Cenury Excellen Talens in Universiy (NCET ), ISVISION Technologies Co. Ld.

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