Robust Face Alignment for Illumination and Pose Invariant Face Recognition

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1 Robust Face Algnment for Illumnaton and Pose Invarant Face Recognton Fath Kahraman 1, Bnnur Kurt 2, Muhttn Gökmen 2 Istanbul Techncal Unversty, 1 Informatcs Insttute, 2 Computer Engneerng Department Ayazağa, İstanbul, Turkey kahraman@be.tu.edu.tr, {gokmen, bkurt}@tu.edu.tr Abstract In buldng a face recognton system for real-lfe scenaros, one usually faces the problem that s the selecton of a feature-space and preprocessng methods such as algnment under varyng llumnaton condtons and poses. In ths study, we developed a robust face algnment approach based on Actve Appearance Model (AAM) by nsertng an llumnaton normalzaton module nto the standard AAM searchng procedure and nsertng dfferent poses of the same dentty nto the tranng set. The modfed AAM search can now handle both llumnaton and pose varatons n the same epoch, hence t provdes better convergence n both pont-to-pont and pont-to-curve senses. We also nvestgate how face recognton performance s affected by the selecton of feature space as well as the proposed algnment method. The expermental results show that the combned pose algnment and llumnaton normalzaton methods ncrease the recognton rates consderably for all featurespaces. 1. Introducton Face recognton systems have matured from the systems workng only n hghly controlled ndoor envronments to the systems allowng dentfcaton of ndvduals n ndoor or outdoor envronments under severe condtons. But some problems stll reman, constranng ther success to a lmted degree. Largely llumnaton and pose varatons are responsble for dramatc varatons on the appearance of the same ndvdual. Hence any mprovement n face appearance wll enhance the recognton performance. These varatons lead to complex effects mposed on the acqured face mage that pertans lttle to the actual dentty. Face recognton systems are usually requred to handle hghly varyng llumnaton and pose condtons. As face recognton technques advance, more researchers have focused on solvng ssues arsng from llumnaton and pose n one shot. Face algnment s a very mportant step to extract good facal features to obtan hgh performance n face recognton, expresson analyss and face anmaton applcatons. Several face algnment methods were proposed: Actve Shape Models (ASM) [1] and Actve Appearance Models (AAM) [2] [3], proposed by Cootes et al are two successful models for object localzaton. ASM uses local appearance models to fnd the canddate shape and global model to constran the searched shape. AAM combnes the constrants on both shape and texture varatons n ts characterzaton of facal appearance. In searchng for a soluton, t assumes lnear relatonshps between appearance varaton and texture varaton and between texture varaton and poston varaton. In ths study, we have used AAM to solve the pose-nvarant face algnment problem. Image varaton due to lghtng changes s larger than that due to dfferent personal denttes. Because lghtng drecton changes alter the relatve gray scale dstrbuton of face mage. Consequently, llumnaton normalzaton s requred to reach acceptable recognton rates. Varyng llumnaton s a dffcult problem and has receved much attenton n recent years. Two studes among them are very mportant: symmetrc shape from shadng [4] and llumnaton cones [5] where face mage varatons due to lght drecton changes are theoretcally explaned. In the later algorthm, both self shadow and cast-shadow were consdered and ts expermental results outperformed most of the exstng methods. The major drawbacks of the llumnaton cone model are the computatonal cost and the strct requrement of seven nput mages per person. Basr et al [6] represent lghtng usng a sphercal harmonc bass wheren a low-dmensonal lnear subspace s shown to be qute effectve for recognton. The harmonc mages can easly be computed analytcally gven surface normals and the albedos. Shashua [7] employ a very smple and practcal mage rato method to map the face mages nto dfferent lghtng condtons. There are several recent mage-based studes on llumnaton nvarant face recognton. Image-based methods are known to be robust to llumnaton varatons [8]. Man drawback of the mage-based methods s that they always assume the face mage s already algned. Usually t s not an easy assumpton to satsfy especally when the nput mage s poorly llumnated. AAM s known to be very senstve to llumnaton, partcularly f the lghtng condtons durng testng are sgnfcantly dfferent from the lghtng condtons durng tranng. Several varatons of AAM appear n the lterature to /07/$ IEEE

2 mprove the orgnal algorthm, namely vew-based AAM [9], Drect Appearance Models [10]. Despte the success of these methods, problems stll reman to be solved. Moreover, under the presence of partal occluson, the PCA-based texture model of AAM causes the reconstructon error to be globally spread over the mage, thus degradng algnment. In ths paper, we propose an approach based on hstogram-fttng to overcome the problem explaned above. A detaled explanaton of the proposed approach s gven n Secton.2. Yet another ssue related to face recognton s to recognze dfferent poses of the same person. Posenvarant face recognton requres pose algnment where mages are ether captured by multple cameras or by a sngle camera at dfferent tme nstances. There are several works related to pose normalzaton. Blanz and Vettel [11] use a statstcal 3D morphable model to tackle wth pose and llumnaton varatons. Snce ther method requres textured 3D scans of heads, t s computatonally expensve. Cootes et al constructed three AAMs whch are called as Vew-based AAMs [9]. These models are lnear model of frontal, profle and half profle vews of faces. They also show how to estmate the pose from the model parameters. The approach n ths study dffers from ther method n the way that we construct only one AAM rather than three models. The dea here s to reduce three searchng procedures to only one fttng procedure by usng one statstcally powerful model to generalze pose varatons. In order to do that we traned one lnear model by usng a tranng dataset consstng of 8 dfferent poses of 3 ndvduals captured under smlar llumnaton condtons. In Secton 3, we wll study the AAM capable of producng dfferent poses of unseen person and show how we project a non-frontal face to a frontal face. In ths paper, we focus on the problems nduced by varyng llumnaton and poses. Our prmary am s to elmnate the negatve effect of llumnaton and pose on the face recognton system performance through llumnaton and pose-nvarant face algnment based on Actve Appearance Model. The rest of the paper s structured as follows: Secton 2 ntroduces Actve Appearance Model (AAM) and llumnaton normalzaton nserted nto the searchng procedure of AAM. Secton 3 s for the proposed pose nvarant combned actve appearance model. The expermental results and the concluson are presented n Secton 4 and 5, respectvely. 2. Actve Appearance Model Actve Appearance Models (AAM) are generatve models capable of syntheszng mage of a gven object class. By estmatng a compact and specfc bass from a tranng set, model parameters can be adjusted to ft unseen mages and hence perform mage nterpretaton. The modeled object propertes are usually shape and pxel ntenstes. Tranng objects are defned by markng up each example mage wth pont of correspondence. AAMs can be rapdly ftted to unseen mages, gven a reasonable ntalzaton. AAM works accordng to the followng prncple: A face mage s marked wth n landmark ponts. The content of the marked face s analyzed based on a Prncpal Component Analyss (PCA) of both face texture and face shape. Face shape s defned by a trangular mesh and the vertex locatons of the mesh. Mathematcally the shape model s represented as follows: x = [ x, x,, x, y, y,, y ]. Face texture s the 1 2 n 1 2 n ntenstes on these landmarks (color pxel values normalzed to shape) and s represented wth the formula (g). Face shape and texture are reduced to a more compact form through PCA such that x = x+φ b and s s g = g +Φ b. In ths form, Φ g g s contans the t egenvectors correspondng to the largest egenvalues and b s s a t- dmensonal vector. By varyng the parameters n b s, the shape can be vared. In the lnear model of texture, Φ g s a set of orthogonal modes of varaton and b g s a set of grey-level parameters. To remove the correlaton between shape and texture model parameters, a thrd PCA s appled on the combned model parameters such that 1 x = x+φ W Qc and g = g +Φ Q c where s s s g g T b = Wb s s b g and T b = Qs Q g c. In ths form, W s s a dagonal matrx of weghts for each shape parameter, allowng for the dfference n unts between the shape and the grey models; c s a vector of appearance parameters controllng both the shape and the grey-levels of the model. Q s and Q g are the egenvectors of the shape and texture models respectvely. (a) (b) Fgure.1 Face algnment usng standard AAM under good and extreme llumnaton. (a) Normal llumnaton, (b) Extreme llumnaton We propose an llumnaton normalzaton method n order to ncrease the accuracy of AAM appled to mages captured under dfferent llumnaton condtons by nsertng an llumnaton normalzaton module nto the standard AAM searchng procedure. The problem s demonstrated n Fg.1. In Fg.1 (a) a correct AAM search result s shown where the nput mage contans a frontal face llumnated frontally.

3 2.1. Illumnaton Normalzaton We dscuss here two lght normalzaton methods and we analyze ther behavor when used n AAM searchng. The frst proposed method s rato-mage [12] face llumnaton normalzaton method. Rato-mage s defned as the quotent between a face mage whose lghtng condton s to be normalzed and a reference face mage. These two mages are blurred usng a Gaussan flter, and the reference mage s then updated by an teratve strategy n order to further mprove the qualty of the restored face. Usng ths llumnaton restoraton method, a face mage wth arbtrary llumnaton can be restored to a face havng frontal llumnaton. The second normalzaton method dscussed n ths study s based on mage hstogram technques. The global hstogram equalzaton methods used n mage processng for normalzaton only transfers the holstc mage from one gray scale dstrbuton to another. Ths processng gnores the face-specfc nformaton and cannot normalze these gray level dstrbuton varatons. To deal wth ths problem, researchers have made many mprovements n recent years. The problem s that well-lt faces do not have a unform hstogram dstrbuton and ths process gves rse to an unnatural llumnaton to the face. As suggested n [13], t s possble to normalze a poorly llumnated mage va hstogram fttng to a smlar, well llumnated mage. In ths study we used a specal type of hstogram fttng algorthm for face llumnaton normalzaton. We make our analyss on one partcular case where one sde of the face s dark and the other sde s brght. The man dea here s to ft the hstogram of the nput face mage to the hstogram of the mean face. The face s frst broken nto two parts (left/rght) and then the hstogram of each wndow s ndependently ftted to the hstogram of mean face. For these two hstograms, namely the hstogram of the left wndow denoted as H l () and the hstogram of the rght wndow denoted as H r (), two mappng functons are computed: f Hl G and f Hr G correspondng to the left and rght wndows. Here G() s the hstogram of the reference mage also called mean face n AAM. An artfact ntroduced by ths mappng s the sudden dscontnuty n llumnaton as we swtch from the left sde of the face to the rght sde. The problem s solved by averagng the effects of the two mappng functons wth a lnear weghtng that slowly favors one for the other as we move from the left sde to the rght sde of the face. Ths s mplemented wth the mappng functon fhtotal G defned as bellow: f H () () (1 ) (). total G = leftness fhl G + leftness fhr G Lghtng normalzaton result s shown n Fg.2 obtaned by usng the hstogram fttng method explaned above together wth the hstogram plots. Fg.2 (a) shows the reference mage and ts hstogram. The normalzaton algorthm s appled to the nput mage gven n Fg.2 (b). The normalzaton result s shown n Fg.2 (c) where the hstogram of the restored mage s very close to the hstogram of the reference mage as expected. As t can be seen from Fg.3 and Fg.4 the normalzaton method can produce more sutable mages to be used n AAM search mechansm. The classcal AAM search fals for all mages gven n the frst row of Fg.3. We wll show n the next secton that AAM search procedure can now converge to the correct shape for the restored mage both n pont-to-pont error and pont-tocurve sense. Fg.4 presents several results obtaned for Set 4 (left) and Set 3 (rght) faces of dfferent ndvduals havng extremely dark and brght regons. A sgnfcant amount of mprovement n the qualty can be easly verfed from the expermental results. The dark parts now become somehow nosy whereas there are stll some very brght areas. (a) (b) (c) Fgure.2 Lght normalzaton usng hstogram fttng: (a) Mean face and ts hstogram, (b) Test face and ts hstogram, (c) Normalzed face and ts hstogram. Fgure.3 Lght normalzaton results: On the top the nput mages are gven, and on the bottom the normalzed mages are shown Expermental Results AAM combnes the shape and texture model n one sngle model. The algnment algorthm (also called AAM searchng) optmzes the model n the context of a test mage of a face. The optmzaton crteron s the error between a syntheszed face texture and the correspondng texture of the test mage.

4 Fgure.4 Lght normalzaton results for extreme cases: On the top the nput mages are gven, and on the bottom the normalzed mages are shown. Due to the llumnaton problems the error can be hgh and the classc searchng algorthm fals. In the proposed approach, we normalze the correspondng texture n the test mage just before we compute the error. We tested the proposed method on the Yale-B [14] face dataset. The total number of mages under dfferent lghtng condtons for each ndvdual s 64. The database s portoned nto four sets dentfed as Set 1-4. Set 1 contans face mages whose lght drecton s less than ± degrees. Set 2 contans face mages whose lght drectons are between ± and ± degrees. Set 3 contans face mages whose lght drectons are between ± and ± degrees. Set 4 contans face mages whose lght drectons are greater than ± degrees. All detals about the Yale B dataset are gven n [14]. We manually labeled 49 mages. To establsh the models, 73 landmarks were placed on each face mage; 14 ponts for mouth, 12 ponts for nose, 9 ponts for left eye, 9 ponts for rght eye, 8 ponts for left eyebrow, 8 ponts for rght eyebrow and 11 ponts for chn. The warped mages have approxmately pxels nsde the facal mask. We constructed a shape space to represent 95% of observed varaton. Then we warped all mages nto the mean shape usng trangulaton. Usng normalzed textures, we constructed a 21-dmensonal texture space to represent 95% of the observed varaton n textures and for shapes we constructed a 12-dmensonal shape space to represent 95% of the observed varaton n shapes. Fnally, we constructed a 15-dmensonal appearance space to represent 95% of the total varaton observed n the combned (shape and texture) coeffcents. Usng a ground truth gven by a fnte set of landmarks for each example, performance can be easly calculated. In a leave-one-out settng, a dstance measure, D(x gt,x), s computed that gves a scalar nterpretaton of the ft between the two shapes,.e. the ground truth (x gt ) and the optmzed shape (x). Two dstance measures defned over landmarks are used to obtan the convergence performance. The frst one s called pont to pont error, defned as the Eucldean dstance between each correspondng landmark: ( ) ( ) 2 2 Dpt. pt. = x xgt, + y ygt, (1) The other dstance measure s called pont to curve error, defned as the Eucldean dstance between a landmark of the ftted shape (x) to the closest pont on the border gven as the lnear splne, r() t = ( rx() t, ry() t ), t [ 0,1], of the landmarks from the ground truth (x gt ): n 1 D ( ()) 2 ( ()) 2 pt. crv. = mn x rx t + y ry t (2) n t = 1 We have calculated these errors for all for datasets (from Set 1 to Set 4). The AAM searchng s known to be very senstve to the selecton of ntal confguraton. We tested the proposed method aganst the selecton of ntal confguraton. We translate, rotate and scale ntal confguratons and see how the proposed method can handle the poor ntalzaton. We made 10 experments for each test mage wth dfferent ntalzatons and took the average error. These experments nclude mean-shape confguraton, ±5 degrees rotaton, scalng by 0.85 and 0.95, translaton by 10% n x and y drectons. Table.1 summarzes the averages of pont-to-pont and pont-tocurve errors when classcal AAM search s used wthout any llumnaton normalzaton. Pont-to-pont and pontto-curve errors obtaned by the proposed llumnaton normalzaton method are much less than the errors obtaned by the classcal AAM (Table.2). Rato-mage method s not sutable for AAM searchng, at least for the frst teratons of the algorthm. Let s suppose that we start searchng n a poston far away from the ground truth locaton. The model syntheszes a face that best fts the current locaton. Then the textures of the syntheszed face and correspondng part n the test mage are analyzed and an error coeffcent s computed, reflectng the smlarty degree of the two textures. We normalze the correspondng texture n the test mage before computng the error. The man problem wth the rato-mage method s that when t s appled to a regon of an mage that s not face-lke, the normalzaton result wll have a lot of nformaton of the mean-face, puttng n other words t wll be mean-face-lke. Thus the error wll be much smaller than the real one, and t wll ntroduce false alarm n the searchng process creatng addtonal local mnma. On the other hand, the hstogram based normalzaton method wll never change the general aspect of an mage, only the pxel ntenstes follow a dfferent dstrbuton. Thus the chances of ntroducng false alarms are reduced usng ths normalzaton method. The ratomage can produce very good results provded that the shape s already algned. But ths s not the case n AAM searchng. We assume that the best ft returned by the searchng algorthm usng hstogram-based normalzaton s a good approxmaton of the real face, and thus the algnment requrement s satsfed.

5 Table.1 Standard AAM fttng performance. Set 1 Set 2 Set 3 Set 4 Pt.pt. 4.9± ± ± ±1.64 Pt.Crv. 2.9± ± ± ±1.44 Table.2 Proposed AAM fttng performance. Set 1 Set 2 Set 3 Set 4 Pt.pt. 4.1± ± ± ±0.58 Pt.Crv. 2.4± ± ± ±0.42 (a) (b) (c) (d) Fgure.5 Searchng results Frst row s the classcal AAM searchng results, second row s the proposed method (a) Intal confguraton (b) Mean face (c) Searchng result obtaned n the 3th teraton (d) Searchng result obtaned n the 6th teraton 3. Pose Normalzaton Pose normalzaton s requred before recognton n order to reach acceptable recognton rates. There are several works related to pose normalzaton. Blanz and Vettel [11] use a statstcal 3D morphable model to tackle wth pose and llumnaton varatons. Snce ther method requres textured 3D scans of heads, t s computatonally expensve. Cootes et al constructed three AAMs whch are called as Vew-based AAMs [9]. We developed AAM based pose normalzaton method whch uses only one AAM. There are two mportant contrbutons over the prevous studes. By usng the proposed method:. One can synthetcally generate appearances for dfferent poses when only frontal face mage s avalable.. One can generate frontal appearance of the face when there s only non-frontal face mage s avalable. Next secton explans the proposed pose normalzaton and generaton method Pose Generaton from 2D Images The same varaton n pose mposes smlar effect on the face appearance for all ndvduals. Deformaton mostly occurs on the shape whereas the texture s almost constant. Snce the number of landmarks n AAM s constant, the wreframe trangles are translated or scaled as pose changes. So as we change pose, only wreframe trangles undergo affne transformaton but the gray level dstrbuton wthn these trangles remans the same. One can easly generate frontal face appearance f AAM s correctly ftted to any gven non-frontal face of the same ndvdual provded that there s no self-occluson on face. Self-occluson usually s not a problem for angles less than ±45. For 2D pose generaton, we frst compute how each landmark pont translates and scales wth respect to the correspondng frontal counterpart landmark pont for 8 dfferent poses, and obtan a rato vector for each pose. We use the rato vector to create the same pose varaton over the shape of another ndvdual. Appearances are also obtaned through AAM usng synthetcally generated landmarks. These are shown n Fg.6. Frst column n Fg.6 shows the frontal faces and the second column shows appearances for varous poses. It s mportant to note that the generated faces contan no nformaton about the ndvdual used n buldng the rato matrx Tranng AAM for Pose Normalzaton An AAM model traned by usng only frontal faces can only ft nto frontal faces well and fal to ft nto nonfrontal faces. Our purpose here s to enrch the tranng database by nsertng synthetcally generated faces at dfferent poses so that AAM model traned by frontal faces can now converge to mages at any pose. We manually labeled 73 landmarks on 49 mages. Let us denote the landmark ponts on th frontal mage as ((,1,,1 ),(,2,,2),,(,,, )) 0 2 K S = x y x y x K y K R where = 1, 2,, N. N s 49 and K=73 n our database. The shape-rato vector explaned n the prevous subsecton (3.1) s defned between the p-posed shape and the frontal shape as x 0 p,1 y p,1 xpk, y p pk, rp ( S, S ) =,,,, x0,1 y 0,1 x0, K y 0, K Shape of any unseen ndvdual at pose p can now be easly obtaned from frontal shape usng shape-rato vector r p as ˆ p 0 Sunseen = rpsunseen. We synthesze shapes from frontal-vew mages n the database for P=8 dfferent poses as, ˆ p 0 S = r S,=1,2,,10, and p=1,2,..,8. p AAM shape component s constructed from these aggregated shapes S and S by applyng prncpal component ˆ p 0 S = S + Q s where S s the mean shape, Q s analyss as s contans k egenvector of the covarance matrx correspondng to the hghest k egenvalues.

6 (a) (b) Fgure.6 Synthetc pose generaton from frontal face: a) Frontal face, b) Synthetcally generated non-frontal faces. Next step s to warp each face n the tranng database to mean shape ( S ) and apply prncpal component analyss to the texture ths tme as T = T + Qt t where T s called mean face. Any shape (S) and texture (T) can be steadly s = Q T S S and mapped to the AAM subspace as s ( ) T t = Qt ( T T ). AAM s comprsed of both shape (Q s ) and texture (Q t ) subspaces. Any change n face shape leads to a change n face texture and vce versa. Face appearance (A) s dependent on shape and textures. Ths dependency s expressed as A= [ Λ s t] T. In order to explot the dependency between shape and texture modeled by the dagonal matrx (Λ), one further PCA s appled to the shape and texture components collectvely and we obtaned the combned model called appearance model as A= Qaa. Any appearance s obtaned by a smple T multplcaton as a= Q A. a feature spaces n our experments: PCA, LDA. Randomly selected 25 mages of each person from Set 1 dataset are used n tranng. All datasets (Set 1 through Set 4) contan faces of all poses. The remanng faces n Set 1 dataset are used as test data. Recognton rates for two feature spaces (.e., PCA and LDA) n Set 1-4 are plotted n Fg.9 for ncreasng dmensons. The recognton rates obtaned when the orgnal mages are used as nput to the classfer are denoted as ORG-PCA and ORG-LDA. The recognton rates obtaned when the mages restored by RI are used as nput are denoted as RI-PCA and RI-LDA. Fnally, the recognton rates obtaned when the mages restored by HF are used as nput are denoted as HF-PCA and HF-LDA. PCA s known to be very senstve to msalgnment n faces. Our expermental studes also verfy ths behavor. When the orgnal mages are used, the PCA recognton rates for all sets are poor. LDA s more successful for dmensons closer to 9. ORG-PCA reaches to 74.36% at most, whle ORG- LDA reaches to 91.26% at most for Set 1. Ths performance drops to.99% for ORG-PCA and to 41.13% for ORG- LDA for Set 4. One mportant observaton s that AAM algnment wth hstogram fttng always leads to better recognton rates n all test sets (Set 1-4) compared to case where orgnal faces are used and rato-mage normalzaton s used rght after the AAM algnment. Another advantage of the proposed method s that smlar recognton performance s obtaned at lower dmensons. Recognton rate for ORG-LDA s just 32.81% whle LDA performance for the proposed approach (called HF-LDA) s 83.38% when the dmenson s set to 3. ORG- LDA catches ths rate when the dmenson s set to 5. Fgure.7 Randomly syntheszed faces from leadng 5 AAM parameters. In order to show how rch representaton AAM provdes us, we used the frst 5 coeffcents and select random ponts n 5-dmensonal space. The correspondng faces are plotted n Fg.7. Even ths smple experment proves that AAM traned as explaned above can generate pose varatons not governed by any shape rato vector (r p ). We also conducted another experment to see how close we ft nto unseen faces at dfferent poses. Fg.8 summarzes the algnment results for these unseen faces. 4. Expermental Results We also analyze how the proposed algnment method affects the recognton performance. We used the followng Fgure.8 Face algnment result for unseen faces. For the challengng test set,.e. Set 4, both ORG-LDA and ORG-PCA fals. The recognton rate s at most.99% for ORG-PCA and 41.13% for ORG-LDA. On the other hand, HF-PCA reaches at most to 76.% and HF-LDA reaches at most to 82.68%. Ths s a sgnfcant mprovement when compared to the results obtaned wthout applyng any preprocessng (41%). Note that all test sets nclude faces of 8 dfferent poses selected from Yale B dataset.

7 (a) (b) (c) (d) Fgure.9 PCA and LDA recognton rates for Set 1 (a), Set 2 (b), Set 3 (c), and Set 4 (d) when orgnal face (ORG), Rato Image (RI) and the proposed restoraton (HF) are used. Fgure.10 Intalzaton (frst row) and algnment/restoraton results of proposed method (second row) for dfferent pose and llumnaton varatons. 5. Concluson In ths study we developed AAM based face algnment method whch handles llumnaton and pose varatons. The classcal AAM fals to model the appearances of the same dentty under dfferent llumnatons and poses. We solved ths problem by nsertng hstogram fttng based normalzaton nto the searchng mechansm and nsertng dfferent poses of the same dentty nto the tranng set. From the expermental results, we showed that the proposed face restoraton scheme for AAM provdes hgher accuracy for face algnment n pont-to-pont error sense. Recognton results based on PCA and LDA feature spaces showed that the proposed llumnaton and pose normalzaton outperforms standard AAM. 6. Acknowledgement Ths work was partly supported by the Natonal Scentfc and Research Councl of Turkey; project no: EEEAG- 104E121 and the State Plannng Agency (DPT) of Turkey. We would lke to thank Florn S. Telcean for hs contrbutons and suggestons. 7. References [1] T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, Actve Shape Models-ther tranng and applcaton, Computer Vson and Image Understandng, 61(1), pp , [2] T.F. Cootes, G. Edwards, and C.J. Taylor, Actve appearance models, IEEE Trans. on PAMI, vol. 23, no.6, pp , 01. [3] Stegmann M. B., Ersboll B. K., Larsen R., FAME - A Flexble Appearance Modelng Envronment, IEEE Trans. on Medcal Imagng, vol. 22(10), pp , 03 [4] W. Zhao and R. Chellappa, SFS Based Vew Synthess for Robust Face Recognton, Proc. 4th Conf. on Automatc Face and Gesture Recognton, 00. [5] H. Wang, Z. S. L, Y. Wang, W. Zhang, Illumnaton Modelng and Normalzaton for Face Recognton, In Proc. AMFG 03, pp , 03. [6] R. Basr and D. Jacobs, Photometrc Stereo wth General, Unknown Lghtng, CVPR, vol.2, pp , 01. [7] A. Shashua and T. Rkln-Ravv, The Quotent Image: Class-Based Re-Renderng and Recognton Wth Varyng Illumnatons, IEEE Trans. on PAMI, pp , 01. [8] W. Zhao and R. Chellappa, Face Processng: Advanced Modelng and Methods, Academc Press, Elsever, 06. [9] T. Cootes, G. Wheeler, Walker K., and Taylor C., Vew based actve appearance models, Image and Vson Computng, : , 02. [10] X. Hou, S. L, H. Zhang, and Q. Cheng, Drect appearance models, In CVPR, pp , 01. [11] V. Blanz, T. Vetter, A morphable model for the synthess of 3D faces, 26th Conf. on Computer graphcs and nteractve technques, p , [12] D. H. Lu, K. M. Lam, L. S. Shen, Illumnaton nvarant face recognton, Pattern Recognton, 38(10): , 05. [13] T. Jebara, 3D Pose Estmaton and Normalzaton for Face Recognton, B. Thess, McGll Centre for Intellgent Machnes, [14] Yale B Web Ste:

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