Gender Classification of Faces Using Adaboost*

Transcription

1 Gender Classificaion of Faces Using Adaboos* Rodrigo Verschae 1,2,3, Javier Ruiz-del-Solar 1,2, and Mauricio Correa 1,2 1 Deparmen of Elecrical Engineering, Universidad de Chile 2 Cener for Web Research, Deparmen of Compuer Science, Universidad de Chile 3 CMLA, ENS Cachan, France Absrac. In his work i is described a framework for classifying face images using Adaboos and domain-pariioning based classifiers. The mos ineresing aspec of his framework is he capabiliy of building classificaion sysems wih high accuracy in dynamical environmens, which achieve, a he same ime, high processing and raining speed. We apply his framework o he specific problem of gender classificaion. We buil several gender classificaion sysems under he proposed framework using differen feaures (LBP, waveles, recangular, ec.). These sysems are analyzed and evaluaed using sandard face daabases (FERET and BioID), and a new gender classificaion daabase of real-world images. 1 Inroducion The compuaional analysis of face images plays an imporan role in many compuer vision applicaions. Among hem we can menion visual human-compuer ineracion, biomery, video conferencing, human-robo ineracion, surveillance, video summarizing, image/video indexing and rerieval, and drivers monioring. So far, face deecion sysems ha perform wih high accuracy in real world applicaions have been developed [13][9][3][11]. However, face classificaion sysems (gender classificaion, race classificaion, age classificaion, ec.) do no achieve similar performance, specifically when analyzing low-resoluion faces obained under unconrolled condiions (unconrolled illuminaion, non-uniform background, ec.). We aim a revering his siuaion by describing a framework for building robus face classificaion sysems using Adaboos [7] and domain-pariioning [8] based classifiers. Wihin his framework we make use of diverse feaure ypes: (i) recangular feaures (similar o Haar waveles) [12], (ii) LBP-based feaures [2], (iii) wavele feaures [9], and (iv) LBP-based feaures applied over a wavele decomposiion. Some of hese feaures have no been used before in face classificaion problems. We apply his learning framework o he specific problem of gender classificaion. Gender classificaion is a relevan problem wihin many applicaions: (1) humanrobo ineracion and visual human-compuer ineracion: i allows building sysems ha behaves differenly depending on he user s gender, (2) video summarizing, and image/video indexing and rerieval: i allows he use of gender informaion in he * This research was funded by Millenium Nucleus Cener for Web Research, Gran P F, Chile. J.F. Marínez-Trinidad (Eds.): CIARP 2006, LNCS 4225, pp , Springer-Verlag Berlin Heidelberg 2006

2 Gender Classificaion of Faces Using Adaboos 69 rerieving/indexing process, and (3) face recogniion biomeric sysems: i allows improving he sysem performance by having specific models for each gender. We buil several robus gender classificaion sysems using his learning framework and diverse feaures. Their main srenghs are he abiliy of processing low-resoluion faces (up o 24x24 pixels), and being illuminaion invarian (no preprocessing is needed for phoomeric normalizaion). These sysems are analyzed and evaluaed using sandard face daabases (FERET, BioID), and a new daabase of realworld images creaed wih his purpose (UCHGender DB). The aricle is srucured as follows. In secion 2 some relaed work is oulined. The learning framework is presened in secion 3. In secion 4 he employed feaures are described. In secion 5 a comparaive analysis of differen gender classificaion sysems is presened. Finally, in secion 6, some conclusions of his work are given. 2 Relaed Work Several mehods have been proposed for solving he gender classificaion problem, among hem sysems based on neural neworks (RBF, back propagaion, ec.), PCA projecions, decision rees, SVM classifiers, and Adaboos classifiers can be menioned. Bes repored resuls have been obained using SVM and Adaboos. We will analyze some of hese relevan works. In [5] i is proposed a gender classificaion sysem based on he use of SVM classifier. The employed feaures are he pixel elemens hemselves. The obained resuls are very good, 3.38% overall error rae when using a RBF kernel, bu he es se consiss of only 259 faces. In [1] is proposed a gender classificaion sysem based on a SVM classifier and feaures obained using PCA (Principal Componen Analysis), CCA (Curvilinear Componen Analysis) and SOM (Self Organizing Maps). Bes resuls are obained using 759 PCA componens, 7.75% overall error rae, bu he size of he faces is resriced o 128x128 pixels and he es se is composed by only 80 faces. One of he drawbacks of he SVM based sysem is he fac ha hey are no realime. Therefore sysems based in Adaboos have been proposed in he las years. In [10] is presened a gender classificaion sysem ha uses a hreshold-weak-classifier based Adaboos algorihm and recangular feaures. The sysem achieves a performance of 79% correc rae in a se of face images obained from Inerne ha were manually annoaed prior o classificaion (500 feaures were employed in his case). This sysem was favorably compared agains he one proposed in [5] using he same daase; i is 1,000 imes faser and i has a higher classificaion rae (79% agains 75.5%). In [14] is described a LUT-based Adaboos sysem for gender classificaion ha uses recangular feaures. Prior o classificaion faces are aligned. This is done using a face alignmen mehod called SDAM ha is a kind AAM (Acive Appearance Model). Afer alignmen, grey-level normalizaion (hisogram equalizaion) is performed. The sysem achieves a classificaion rae of 88% in images downloaded form Inerne (using 36x36 face windows), and using his daabase i is favorably compared agains a SVM-based sysem and a hreshold-adaboos sysem. Our Adaboos-based gender classificaion sysem employs he domain-pariioning approach, and he main improvemens over previous works are: (1) he use of more suiable feaures for addressing his problem (mlbp feaures behave beer han

3 70 R. Verschae, J. Ruiz-del-Solar, and M. Correa recangular feaures; see secion 5), (2) he usage of smaller face windows (24x24) which allows analyzing smaller faces, and (3) a faser processing, because, besides he eye alignmen, we do no perform any geomeric or phoomeric normalizaion. 3 A Learning Framework for Building Robus Face Classificaion Sysems The key conceps used in he considered framework are boosing and domain pariioning classifiers. Adaboos [7] (a Boosing algorihm) is employed for finding highly accurae hypoheses (classificaion rules) by combining several weak hypoheses (classifiers). We use domain pariioning weak hypoheses [8], each one having a moderae accuracy, and giving self-raed confidence values ha esimae he reliabiliy of each predicion. These weak classifiers are linearly combined, obaining a classifier of he form shown in (1). Each funcion h (x) is a weak classifier, T is he number of weak classifiers, and b is a hreshold value ha defines he operaion poin of he classifier. The class assigned o he inpu corresponds o he sign of H(x). T H (x) = α h (x) b (1) =1 The weak classifiers are applied over feaures compued in every paern o be processed. Each weak classifier has associaed a single feaure. Following [8], domain-pariioning weak hypoheses make heir predicions based on a pariioning of he domain X ino disjoin blocks X 1,,X n, which cover all X, and for which h(x)=h(x ) for all x, x X j. Thus, he weak classifiers predicion depends only on which block X j a given sample insance falls ino. In our case he weak classifiers are applied over feaures, herefore each feaure domain F is pariioned ino disjoin blocks F 1,,F n, and a weak classifier h will have an oupu for each pariion block of is associaed feaure f: h( f (x)) = c j f (x) F j (2) For each classifier, he value associaed o each pariion block (c j ), i.e. is oupu, is calculaed for minimizing a bound of he raining error. This value depends on he number of imes ha he corresponding feaure, compued on he raining samples (x i ), fall ino his pariion block (hisograms), and on he class of hese samples (y i ) and heir weigh D(i). For minimizing he raining error, c j is se o [8]: c j = 1 2 ln W j +1 +ε W j 1 +ε, W j l = D(i), where l =±1 (3) i:f (x i ) F j y i = l were ε is a regularizaion parameer. The oupus, c j, of each he weak classifier, obained during raining, are sored in a LUT for speeding up is evaluaion. The pseudo code of he whole algorihm is shown in figure 1.

4 Gender Classificaion of Faces Using Adaboos 71 Given ( x, y ),...,( x, y ) were x X, y Y = { 1, + 1} D1 ( i) 1, i = 1,..., m m for = 1:T do H( x) = 1 normalized () o a p.d.f W h = p(j) ln 2 W = 2 j + 1 j -1 j ε, j = 1,..., J + ε j 1 H, p = 1,..., P do selec h for which Z is minimized: h = argminz D T 1 = 1 Z p for each feaure f F, h m ( i) D ( i)exp( y h ( x )), i = 1,..., m h ( x) j m p W W i p i i i hp H p Fig. 1. Domain-Pariioning Adaboos raining algorihm 4 Feaures We analyze he use of differen kinds of feaures applied o he problem of face classificaion: recangular, mlbp, wavele, and wavele+mlbp feaures. In all cases he feaure space is pariioned so i can be used direcly wih he domain-pariioning Adaboos classifier described in secion 3. In all cases LUTs (Look-Up Tables) are used for a fas evaluaion of he weak classifiers. 4.1 Recangular Feaures Recangular feaures resemble Haar waveles and can be evaluaed very quickly, independenly of heir size and posiion, using he inegral image [12]. They correspond o he difference beween sums of pixels values in recangular image regions. The oupu value defines a domain ha is pariioned using inervals (or bins) of equal size [13]. 4.2 Modified LBP The LBP (Local Binary Paern), also known as exure number or census ransform, corresponds o an illuminaion invarian descripor of he local srucure in a given image neighborhood. We use heir modified version [2] (mlpb = modified LBP), which overcomes some problems of he original LBP. The mlbp is compued as follows: for a given window of 3x3 pixels, he average of he pixels in he window is calculaed. Then, each of he pixel values is compared agains he obained average. From hese comparisons, 9 bis are generaed, wih 0 indicaing ha a pixel is smaller

5 72 R. Verschae, J. Ruiz-del-Solar, and M. Correa han he average, and 1 oherwise. Afer ha, he concaenaed 9 bis corresponds o he mlbp feaure. As i can be noiced, for he mlbp feaure he domain pariion is already defined, here are 512 bins (acually 511 [2]). 4.3 Wavele Coefficiens The wavele ransformaion allows analyzing images in he spaial-frequency domain. In he conex of face deecion, waveles coefficiens have been successfully employed as local feaures. As in [9], he feaures correspond o groups of 8 neighbor coefficiens (in space, orienaion or scale) ha are used as a pariion of he feaure space. Each of he coefficiens is quanized in 3 levels and hen grouped ogeher, defining a pariion of 6,561 bins. Given ha we use small raining ses (see secion 5), such a large number of bins may lead o overfiing. Therefore, we pariion he wavele coefficiens direcly in a small number of bins (16, 32 and 64). 4.4 Modified LBP of he Wavele Coefficiens The mlbp is applied over he wavele ransform for avoiding he quanizaion of he wavele coefficiens, and for summarizing informaion from groups of coefficiens. In his way we can reduce he number of pariions o 511 bins. 5 Evaluaion 5.1 Evaluaed Gender Classificaion Sysems We use hree baseline sysems for comparing he performance of our Adaboos based gender classificaion sysems: - SVM: SVM classifier and face pixels as feaures. Parameers: RBF kernel, σ =1,125, C=25, 3,082 suppor vecors. - SVM+PCA: SVM classifier and PCA projecion of he face pixels as feaures. Parameers: RBF kernel, σ =4,950, C=50, 1,981 suppor vecors, and 300 PCA componens. - PCA: Face gender is deermined using he minimal reconsrucion error afer projecion of he face pixels in a PCA model of men faces and a PCA model of women faces. Parameers: 100 PCA componens in each model. We build differen gender classificaion sysems using he described learning framework (domain pariioning Adaboos) and differen feaures: - Adaboos-Rec: Adaboos & 1,000 recangular feaures, 16 bins. - Adaboos-mLBP: Adaboos & 1,000 mlbp feaures. - Adaboos-Wav: Adaboos & 426 wavele-based feaures (2-level W), 64 bins. - Adaboos-Wav-mLBP: Adaboos & 958 wavele+mlbp feaures (3 levels WT). For all sysems he selecion of he bes parameers was done using a validaion daabase (see nex secion).

6 Gender Classificaion of Faces Using Adaboos Daabases For he classifiers raining (Adaboos, PCA and SVM based) we buil a raining daabase of 4,245 face images, conaining images from he CAS-PEAL daase [4] and UCHGenderTrainDB (our own daase). We use 2,009 images from CAS-PEAL (1,222 men and 787 women), and 2,165 images from UCHGenderTrainDB (1,150 men and 1,115 women). We use a validaion daabase of 2,745 face images, conaining images from CAS-PEAL and UCHGenderValDB (our own daase). The validaion CAS-PEAL daase conains 1,312 images (768 men and 544 are women), while UCHGenderValDB conains 1,433 images (744 men and 689 women). The validaion daabase is used for model selecion during raining. In he case of domainpariioning Adaboos, i is used for selecing he number of weak classifiers and he number of bins in he LUTs. In he case of SVM, i is used for selecing he parameers of he kernel, while in he case of PCA, for selecing he number of componens. In boh daabases, raining and validaion, for each face a second one was generaed, which corresponds o a random variaion in he posiion of cropping. I is imporan o noe ha CAS-PEAL includes only Asians, while UCHGenderTrainDB considers oher races. Wih his combinaion we inend o make our gender classifier race independen o a large degree. For evaluaing he proposed sysem we use 3 daabases: (1) he UCHGender daabase (real world images, see fig. 2), (2) he Fere daabase [6], and (3) he BioID daabase [15]. See able 1 for deails on he number of images in each class for hese daases. No image in he evaluaion daabase is also included in he raining or in he validaion daabases. Faces were cropped using he same procedure during raining and evaluaion. The cropping was done using he posiion of he eyes. In he case of he raining and validaion daases, he cropping of he faces was done using ground ruh daa, while in he case of evaluaion wo cases are considered: cropping using ground ruh daa and cropping using auomaic face and eyes deecion (boh sysems are described in [11]). The obained resuls using boh alernaives are analyzed (see figs. 3-5). Table 1. Summary of Daabases used for evaluaion Tes daabase # images # Faces # Men # Women % Men % Women UCHGender Fere 2,745 2,745 1,650 1, BioID 1,521 1, The images employed for he raining and esing of he SVM and Adaboos based sysems were no preprocessed a all. In he case of he PCA based sysems (PCA and PCA+SVM) he sandard preprocessing required by PCA analysis was employed (subracion of he mean face image plus variance normalizaion).

7 74 R. Verschae, J. Ruiz-del-Solar, and M. Correa (a) (b) Fig. 2. UCHGender DB (examples) (24x24 pixels): (a) Men, (b) Women faces 5.3 Resuls All he here presened resuls, wih he excepion of PCA and PCA+SVM, consider faces of 24x24 pixels. PCA based mehods use faces of 100x185 pixels. The usage of larger face sizes (48x48) slighly improves he performance of some mehods (for example Adaboos-Rec feaures and Adaboos-Wav), however he raining ime increases exponenially (from hours o days). Because of his and for no inroducing imporan resricions in he size of he faces o be analyzed we consider faces of 24x24 pixels. Figures 3, 4 and 5 show he resuls of he evaluaion of he differen mehods in he Fere, BioID and UCHGender daabases, respecively. Figures 3(a), 4(a) and 5(a) show he resuls in he case when he eyes were annoaed, while figures 3(b), 4(b) and 5(b) show resuls when he eyes (and faces) were auomaically deeced. In his las case only correc face deecions were considered in he saisics (% of correc face deecions: Fere: 99.49%, BioID: 98.22% and UCHGender: 96.79%). Table 2 shows some numerical resuls for he case of equal error raes in boh classes. Table 2. Correc Classificaion Raes a Operaion poins wih equal error raes in boh classes. Only bes performing mehods are shown. Faces were cropped using annoaed eyes (lef), and auomaically deeced eyes (righ). Resuls are separaed by a /. Bes resuls are shown in bold. Daabase SVM (RBF) Adab.-Rec Adab.-mLBP Adab.-Wav Adab.-Wav-mLBP UCHGender 79.2 / / / / / Fere 83.4 / / / / / BioID / / / / / I can be noiced ha in he Fere daabase (fig. 3) he bes performing mehod is Adaboos-mLBP followed by Adaboos-Rec, SVM and SVM+PCA. I can also be noice ha in his daabase he resuls of all mehods are relaively independen of he way he eye posiions were obained. Main reason seems o be he fac ha due o he characerisics of his daabase (homogeneous backgrounds and conrolled illuminaion) he face deecion rae is very high (99.49%) and he eyes deecion very precise. In he case of he BioID daabase (fig. 4), he performance of some of he mehods increases when he eye deecor is used. This happens in paricular wih he AdaboosmLBP and Adaboos-Rec. When using annoaed eyes (fig. 4(a)), bes performing mehods are Adaboos-mLBP and SVM depending on he operaion poin. This is he

8 Gender Classificaion of Faces Using Adaboos 75 only case where SVM works beer han he oher mehods, for equal error raes in boh classes (see able 2). When using auomaic deeced eyes (fig. 4(b)), he bes performing mehod is Adaboos-mLBP followed by Adaboos-Rec. This shows ha Adaboos-mLBP is more robus (an independen) of he eye deecor being used. In he case of he UCHGender daabase (fig. 5), i can be noiced ha again he Adaboos-mLBP ouperforms oher mehodologies. In he case of UCHGender daabase we do no include resuls of he PCA and PCA+SVM mehods because mos faces are smaller han 100x185, and PCA using hose images gives poor resuls. From figs 3, 4 and 5 i can be noiced ha bes resuls are obained in he Fere daabase, followed by BioID, and UCHGender. This is probably because he Fere daabase conains homogeneous backgrounds, conrolled illuminaion, and only compleely fronal faces. On he oher hand, he UCHGender conains a large variaion on backgrounds, races, illuminaion condiions, and faces are no necessarily fronal -- some of hem presen yaw (ou-of-plane) roaion. In able 3 i is shown he average ime required by he differen mehods for he gender classificaion of a given face image. This ime does no include he ime required for he face deecion/cropping, face scaling and eyes deecion. I includes jus he ime required for he face analysis (feaure exracion and classificaion). I can be seen ha Adaboos-mLPB is abou 10 imes faser han SVM, while Adaboos-Rec is 6 imes faser han Adaboos-mLPB, and 60 imes faser han SVM. This evaluaion was done in an Inel Penium 4 CPU 1.80GHz wih 2GB RAM, running Debian GNU/Linux. (a) (b) Fig. 3. Classificaion raes for he FERET daase. Faces were aligned using: (a) annoaed eyes, (b) deeced eyes. Table 3. Processing imes of some of he differen mehods Mehod SVM PCA SVM+PCA Ada-Rec Ada-mLBP Ada-Wav Ada-Wav-mLBP Time [mseg]

9 76 R. Verschae, J. Ruiz-del-Solar, and M. Correa (a) (b) Fig. 4. Classificaion raes for he BioID daase. Faces were aligned using: (a) annoaed eyes, (b) deeced eyes. (a) (b) Fig. 5. Classificaion raes for he UCHGender daase. Faces were aligned using: (a) annoaed eyes, (b) deeced eyes. 6 Conclusions In his aricle i was presened a framework for classifying face images using Adaboos and domain-pariioning based classifiers. We buil several gender classificaion sysems using he proposed framework and differen feaures (LBP, waveles, recangular, ec.). These sysems are analyzed and evaluaed using hree daabases (Fere, BioID and UCHGender). The obained resuls indicae ha Adaboos-mLBP

10 Gender Classificaion of Faces Using Adaboos 77 ouperforms all oher Adaboos-based mehods, as well as baseline mehods (SVM, PCA and PCA+SVM), in erms for classificaion rae. The Adaboos-mLBP behavior is robus o changes in he way eyes posiions are obained for performing he face alignmen. The mos ineresing advanage of he Adaboos-based mehods is is high accuracy in dynamical environmens, achieved wih high processing speed. In erms of processing ime, he faser mehod is Adaboos-Rec, being al leas 6 imes faser han he oher mehods (60 imes faser han SVM-based mehods). I is followed by Adaboos-mLBP, which is 10 imes faser han SVM-based mehods. Anoher ineresing characerisic of he developed Adaboos-based mehods is heir relaively high raining speed, abou one hour in he case of Adaboos-mLBP and abou 48 hours for Adaboos-Rec, for a raining daabase of 4,245 face images and a validaion daabase of 2,745 face images. Fuure work can be done in exending his framework o muli-class problems (age and race classificaion), and finding ou a way of using (selecing) differen kinds of feaures a he same ime. Acknowledgemens Porions of he research in his paper use he FERET daabase of facial images colleced under he FERET program, and he CAS-PEAL face daabase colleced under he sponsor of he Chinese Naional Hi-Tech Program and ISVISION Tech. Co. Ld. References 1. S. Buchala, N. Davey, R. J. Frank, T.M. Gale, M. Loomes, W. Kanargard, Gender Classificaion of Face Images: The Role of Global and Feaure-Based Informaion, ICONIP 2004, Calcua, India, Lecure Noes in Compuer Science 3316, pp B. Fröba and A. Erns, Face deecion wih he modified census ransform, 6h In. Conf. on Face and Gesure Recogniion - FG 2004, pp , Seoul, Korea, May M. Delakis and C. Garcia, Convoluional face finder: A neural archiecure for fas and robus face deecion, IEEE Trans. Paern Anal. Mach. Inell., Vol. 26, No. 11, pp , W. Gao, B. Cao, S. Shan, D. Zhou, X. Zhang, D. Zhao, The CAS-PEAL Large-Scale Chinese Face Daabase and Evaluaion Proocols, Technical Repor No. JDL_TR_04_FR_001, Join Research & Developmen Laboraory, CAS, B. Moghaddam, M.-H. Yang, Learning Gender wih Suppor Faces, IEEE Trans. Paern Anal. Mach. Inell., Vol. 24, No. 5, pp , P. J. Phillips, H. Wechsler, J. Huang and P. Rauss, The FERET daabase and evaluaion procedure for face recogniion algorihms, Image and Vision Compuing J., Vol. 16, no. 5, pp , R.E. Schapire, A brief inroducion o boosing, In Proceedings of he Sixeenh Inernaional Join Conference on Arificial Inelligence, R.E. Schapire and Y. Singer, Improved Boosing Algorihms using Confidence-raed Predicions, Machine Learning, 37(3): , 1999.

11 78 R. Verschae, J. Ruiz-del-Solar, and M. Correa 9. H. Schneidermann and T. Kanade, A saisical model for 3D objec deecion applied o faces and cars, IEEE Conf. on Compuer Vision and Paern Recogniion, Vol. 1, pp , G. Shakhnarovich, P. Viola, and B. Moghaddam, A Unified Learning Framework for Real Time Face Deecion & Classificaion, In Conf. on Auomaic Face & Gesure Recogniion FG 2002, pp , May R. Verschae, J. Ruiz-del-Solar, M. Correa, and P. Vallejos, A Unified Learning Framework for Face, Eyes and Gender Deecion using Nesed Cascades of Boosed Classifiers, Technical Repor UCH-DIE-VISION , Dep. of E. Eng., U. de Chile, P. Viola and M. Jones, Fas and robus classificaion using asymmeric adaboos and a deecor cascade, Advances in Neural Inform. Processing Sysem 14, MIT Press, B. Wu, H. Ai, C. Huang, and S. Lao, Fas roaion invarian muli-view face deecion based on real Adaboos, 6h In. Conf. on Face and Gesure Recogniion - FG 2004, pp , Seoul, Korea, May B. Wu, H. Ai, and C. HUANG. LUT-based Adaboos for Gender Classificaion, 4h In. Conf. on Audio and Video-based Biomeric Person Auhenicaion, June 10-11, 2003, Guildford, Unied Kingdom. 15. BioID Face Daabase. Available on april 2006 in: hp:// downloads/facedb.php