Facial Expression Recognition Using Sparse Representation

Transcription

1 Facal Expresson Recognton Usng Sparse Representaton SHIQING ZHANG, XIAOMING ZHAO, BICHENG LEI School of Physcs and Electronc Engneerng azhou Unversty azhou CHINA Department of Computer Scence azhou Unversty azhou CHINA Abstract: - Facal expresson recognton s an nterestng and challengng subject n sgnal processng and artfcal ntellgence. In ths paper, a new method of facal expresson recognton based on the sparse representaton classfer (SRC) s presented. wo typcal appearance facal features,.e., local bnary patterns (LBP) and Gabor wavelets representatons are extracted to evaluate the performance of the SRC method on facal expresson recognton tasks. hree representatve classfcaton methods, ncludng artfcal neural network (ANN), K-nearest neghbor (KNN), support vector machnes (SVM), are used to compare wth the SRC method. Expermental results on two popular facal expresson databases,.e., the JAFFE database and the Cohn-Kanade database, demonstrate the promsng performance of the presented SRC method on facal expresson recognton tasks, outperformng the other used methods.. Key-Words: - Sparse representaton, compressve sensng, facal expresson recognton, local bnary patterns, Gabor wavelets representatons, artfcal neural network, K-nearest neghbor, support vector machnes Introducton Emoton conveys the psychologcal state of a human beng, as emoton s expressed by varous physologcal changes, such as changes n heart beat rate, degree of sweatng, blood pressure, and so on. Emoton can be emboded by vocal ntonaton, facal expresson, body gesture and movement. Facal expresson s the most natural and effcent means for human bengs to communcate ther emotons and ntentons, as communcaton s prmarly carred out face to face. In recent years, facal expresson recognton has attracted ncreasng attenton n sgnal processng, computer vson, pattern recognton, and human computer nteracton (HCI) research communtes. One of the most mportant applcatons of facal expresson recognton s to make HCI become more human-lke, more effectve, and more effcent [-3]. Specfcally, such computers wth the ablty of facal expresson recognton could detect and track a user's affectve states and ntate communcatons based on ths nformaton, rather than smply respondng to a user's commands. Generally, a basc automatc facal expresson recognton system conssts of two steps: facal feature extracton and representaton, and facal expresson recognton. Facal feature extracton and representaton s to extract facal features to represent the facal changes caused by facal expressons. In facal feature extracton for expresson analyss, there are manly two types of approaches: geometrc feature-based methods and appearance-based methods [4]. Geometrc features such as the shape and locaton of the eyes, mouth, brows, nose, etc., are the mostly extracted features. he facal components or facal feature ponts are extracted to form a feature vector that represents the face geometry. Fducal facal feature ponts have been wdely adopted as geometrc features for facal representaton. For example, the geometrc postons of 34 fducal ponts on a face are usually used to represent facal E-ISSN: Issue 8, Volume, August 0

2 mages [5, 6]. Appearance-based methods work on the facal mages drectly to represent facal textures such as wrnkles, bulges and furrows, and are thus smple to mplement. he representatve appearance features contans the raw pxels of facal mages, Gabor wavelets representaton [7, 8], Egenfaces [9], and Fsherfaces [0], etc. In recent years, a new face descrptor called local bnary patterns (LBP) [], has been utlzed to descrbe the local appearance of facal expressons wth great success on the facal expresson recognton tasks [-4], due to ts tolerance aganst llumnaton changes and computatonal smplcty. Facal expresson recognton s to use the extracted facal features to recognze dfferent expressons. Dependng on whether the temporal nformaton s consdered, facal expresson recognton approaches can be categorzed as framebased or sequence-based. he frame-based method does not take the temporal nformaton of nput mages nto account, and use the extracted features from a sngle mage to recognze the expresson of that mage. In contrast, the sequence-based method attempts to capture the temporal pattern n a sequence to recognze the expresson for one or more mages. So far, varous classfers, ncludng artfcal neural network (ANN) [5], K-nearest neghbor (KNN) [6], support vector machnes (SVM) [7], and so on, have been appled for frame-based expresson recognton. For sequence-based expresson recognton, the wdely used technques are hdden Markov models (HMM) [8], dynamc Bayesan networks [9], SVM [0]. Among the aforementoned two steps n a basc facal expresson recognton system, facal expresson recognton s the most crtcal aspect for any successful facal expresson recognton system. he performance of a facal expresson recognton system s manly decded by a classfer. herefore, desgnng a good classfer s a crucal step on facal expresson recognton tasks. In recent years, a new theory, Compressve Sensng (CS) [-3], also referred as Compressed Sensng or Compressve Samplng, has been proposed as a more effcent classfcaton method. he newly-emerged CS theory, orgnally amng to address sgnal sensng and codng problems, clams that a sparse sgnal can be recovered from a small number of random lnear measurements. he CS theory has been used to form a new classfcaton technque called sparse presentaton classfer (SRC), showng promsng performance on pattern recognton [4-6]. Motvated by very lttle work done on the applcaton of SRC for facal expresson recognton, n ths paper we nvestgate the performance of SRC on facal expresson recognton tasks. wo representatve appearance features,.e., Gabor wavelets representaton and local bnary patterns (LBP), are extracted n our work. And then the state-of-the-art classfers, ncludng artfcal neural network (ANN), K-nearest neghbor (KNN), support vector machnes (SVM), are used to compare wth SRC. o evaluate the performance of SRC, facal expresson recognton experments are performed on two popular databases,.e., the JAFFE database [8] and the Cohn-Kanade database [7]. he remander of ths paper s organzed as follows. Compressve sensng (CS) s gven n Secton. Secton 3 presents sparse representaton classfer (SRC) n detal. In Secton 4, two popular facal expresson databases are ntroduced. Facal feature extracton s provded n Secton 5. ANN, KNN and SVM s presented n Secton 6. Secton 7 shows the experment results and analyss. Fnally, the conclusons are gven n Secton 8. Compressve Sensng Compressve Sensng (CS) [-3] s a new samplng strategy that uses a fxed set of lnear measurements together wth a non-lnear recovery process. In order for ths scheme to work wth a low number of measurements, the CS theory requres the sensed sgnal to be sparse n a gven orthogonal bass and the sensng vectors to be ncoherent wth ths bass. Mathematcally speakng, gven a system of under-determned equaton: y = A x m< n (), m m n n It s known that the above Eq.() has no unque soluton, snce the number of varables s larger than the number of equatons. In sgnal processng terms, the length of the sgnal ( n ) s larger than the number of samples ( m ). However, accordng to the CS theory, f the sgnal s sparse, t s necessarly unque, and can be reconstructed by practcal algorthms. Suppose that the sgnal s k-sparse f t s a lnear combnaton of only k bass vectors. hat s, there are only k non-zero values n x, and the remander are all zeroes. In ths case, t s possble to fnd the soluton to Eq.() by a brute force enumeraton of all the possble k-sparse vectors of length n. E-ISSN: Issue 8, Volume, August 0

3 Mathematcally speakng, ths problem can be expressed as mn x, subject to y= A x () 0 where s the l 0 0 -norm and denotes the number of non-zero elements n the vector. Eq.() s known to be an NP (non-determnstc polynomal) hard problem, and s thus not a practcal soluton to Eq.(). he CS lteratures [-3] ndcates that under a certan condton on the projecton matrx A,.e., restrcted sometry property (RIP) [8], the sparsest soluton to Eq.() can be obtaned by replacng the l -norm n Eq.() by ts closest convex surrogate, the 0 l -norm ( ). herefore, the soluton to Eq.() s l -norm mnmzaton equvalent to the followng problem mn x, subject to y= A x (3) where the l -norm,, denotes the mnmzaton of the sum of absolute values of elements n the vector, and serves as an approxmaton of the l 0 -norm. In practce, the equalty y= A x s often relaxed to take nto account the exstence of measurement error n the sensng process due to a small amount of nose. Suppose the measurements are naccurate and consder the nosy model y= A x+ e (4) where e s a stochastc or determnstc error term. Partcularly, f the error term e s assumed to be whte nose such that e < ε, where ε s a small constant. A nose robust verson of Eq.(3) s defned as follows mn x, subject to y A x < ε (5) 3 Sparse Representaton Classfer Based on the CS theory, a new classfcaton method,.e., sparse representaton classfer (SRC), has been recently proposed [4-6]. In the SRC algorthm, t s assumed that the whole set of tranng samples form a dctonary, and then the recognton problem s cast as one of dscrmnatvely fndng a sparse representaton of the test mage as a lnear combnaton of tranng mages by solvng the optmzaton problem n Eq.(3) or (5). Formally, for the tranng samples of a sngle class, ths assumpton can be expressed as y = α y + α y + + α y + ε k, test k, k, k, k, k, nk k, nk k nk = α y + ε = k, k, k where y k, test s the test sample of the the th tranng sample of the th k class, k, th k class, k, (6) y s α s the weght correspondng weght and ε k s the approxmaton error. For the tranng samples from all c object classes, the aforementoned Eq.(6) can be expressed as y = α y + + α y + k, test,, k, k, Equvalently, yk, test + α y + + α y + ε k, nk k, nk c, nc c, nc (7) = Aα + ε (8) where A= [ y, y, n y k, yk, n y,, ] k c yc nc ' α. = [ α, α, n α k, α k, n α,, ] k c α c nc he lnearty assumpton n the SRC algorthm coupled wth Eq.(8) mples that the weght vector α should be zero except those assocated wth the correct class of the test sample. o obtan the weght vector α, the followng l0 -norm mnmzaton problem should be solved. mn α, subject to yk test Aα ε (9) α 0, It s known that Eq.(9) s an NP-hard problem. he NP-hard l0 -norm can be replaced by ts closest convex surrogate, the l -norm. herefore, the soluton of Eq.(9) s equvalent to the followng l - norm mnmzaton problem. (0) mn α, subject to yk test Aα ε α, hs s a convex optmzaton problem and can be solved by quadratc programmng. Once a sparse E-ISSN: Issue 8, Volume, August 0

4 soluton of α s obtaned, the classfcaton procedure of SRC s summarzed as follows: Step : Solve the l -norm mnmzaton problem n Eq.(0). Step : For each class, compute the resduals between the reconstructed sample y n ( ) = α y and the gven test sample recons j=, j, j by r( ytest, ) = yk, test yrecons ( ). Step 3: he class of the gven test sample s determned by dentfy( y ) = arg mn r( y, ). test test the Cohn-Kanade database. he selected sequences, each of whch could be labeled as one of the sx basc emotons, come from 96 subjects, wth to 6 emotons per subject. For each sequence, the neutral face and one peak frames were used for prototypc expresson recognton, resultng n 470 mages (3 anger, 00 joy, 55 sadness, 75 surprse, 47 fear, 45 dsgust and 6 neutral). 4 Facal Expresson Database wo popular databases,.e., the JAFFE database [8] and the Cohn-Kanade database [7], are used to perform facal expresson recognton experments. he JAFFE database contans 3 mages of female facal expressons. Each mage has a resoluton of pxels. he head s almost n frontal pose. he number of mages correspondng to each of the seven categores of expressons (anger, joy, sadness, neutral, surprse, dsgust and fear) s roughly the same. A few of them are shown n Fg.. Fg. Examples of facal expresson mages from the Cohn-Kanade database 5 Facal Feature Extracton wo types of facal features,.e., Gabor wavelets representaton and local bnary pattern (LBP), are extracted for facal expresson recognton experments. Fg. Examples of facal expresson mages from the JAFFE database he Cohn-Kanade database conssts of 00 unversty students aged from 8 to 30 years, of whch 65% were female, 5% were Afrcan- Amercan and 3% were Asan or Latno. Subjects were nstructed to perform a seres of 3 facal dsplays, sx of whch were based on descrpton of prototypc emotons. Image sequences from neutral to target dsplay were dgtzed nto pxels wth 8-bt precson for grayscale values. Fg. shows some sample mages from the Cohn-Kanade database. In ths work, we selected 30 mage sequences from 5. Gabor Wavelets Representaton he Gabor wavelets representaton [7, 8] exhbt strong characterstcs of spatal localty and orentaton selectvty, makng them a sutable choce for mage feature extracton when one s goal s to derve local and dscrmnatng features for facal expresson classfcaton. he Gabor wavelet kernels can be defned as kµν, z σ σ kµν, z kµν, ϕ ( z) = e µν, σ [ e e ] () where µ and ν denote the orentaton and scale of the Gabor kernel, z= ( x, y), denotes the norm operator, and the wave vector k µν, s defned as E-ISSN: Issue 8, Volume, August 0

5 φ µν, ν k = k e µ () where k = k / ν max fν and φ / 8 µ = πµ. k max s the maxmum frequency, and f s the spacng factor between kernels n the frequency doman. As done n [8], we used 40 Gabor wavelet kernels at fve scales, ν = {0,,, 4}, and eght orentatons, µ= {0,,,7}, wth σ = π, kmax = π /, and f =. he Gabor wavelets representaton s essentally the concatenated pxels of the 40 modulus-of-convoluton mages obtaned by convolvng the nput mage wth these 40 Gabor kernels. In practce, the magntude of Gabor wavelets representaton s used for facal expresson recognton. As suggested n [9], before concatenaton each output mage s down-sampled by a factor of 6 and normalzed to zero mean and unt varance. Fg.3 he process of LBP extracton 6 Revew of ANN, KNN and SVM o verfy the effectveness of SRC, three typcal classfers,.e., artfcal neural network (ANN), K- nearest neghbor (KNN), support vector machnes (SVM), are employed to compare wth SRC. In ths secton, we separately revew ANN, KNN and SVM n bref. 5. Local Bnary Pattern he local bnary pattern (LBP) operator [] s a gray-scale nvarant texture prmtve statstc, whch has shown excellent performance n the classfcaton of varous knds of textures. For each pxel n an mage, a bnary code s produced by thresholdng ts neghborhood wth the value of the center pxel. he LBP code of the center pxel n the neghborhood s obtaned by convertng the bnary code nto a decmal one. Based on the LBP operator, each pxel of an mage s labeled wth an LBP code. he 56- bn hstogram of the labels contans the densty of each label and can be used as a texture descrptor of the consdered regon. he process of LBP features extracton s summarzed as follows: Frstly, a facal mage s dvded nto several nonoverlappng blocks. Secondly, LBP hstograms are computed for each block. Fnally, the block LBP hstograms are concatenated nto a sngle vector. As a result, the facal mage s represented by the LBP code. Fg.3 presents the process of LBP feature extracton. 6. ANN Artfcal neural network (ANN) s a system derved through models of neuropshychology, and s capable of dentfyng complex nonlnear relatonshps between nput and output data sets. Generally, ANN can be categorzed nto three man basc types: multlayer perceptron (MLP), recurrent neural networks (RNN) and radal bass functons neural networks (RBFNN). In ths work, we use RBFNN [30, 3] to perform facal expresson recognton, snce ts man advantages are computatonal smplcty, supported by well-developed mathematcal theory, and robust generalzaton. For classfcaton, RBFNN s a three layer feedforward network that conssts of one nput layer, one hdden layer and one output layer, as shown n Fg.4. he nput layer receves nput data. Each nput neuron of the nput layer corresponds to a component of an nput vector x.he hdden layer s used to cluster the nput data and extract features. he hdden layer conssts of n neurons and one bas neuron. Each nput neuron s fully connected to the hdden layer neurons except the bas one. Each hdden layer neuron computes a kernel functon. A Gaussan Radal Bass Functon could be a good choce for the hdden layer. he used Gaussan Radal Bass Functon for the hdden layer s defned as E-ISSN: Issue 8, Volume, August 0

6 x c exp( ), =,,, n y = σ, = 0 (3) where c and σ represent the center and the wdth of the neuron, respectvely, and the symbol denotes the Eucldean dstance. he weght vector between the nput layer and the -th hdden layer neuron corresponds to the center c. he closer x s to c, the hgher the value the Gaussan functon wll produce. he output layer conssts of m neurons correspondng to the possble classes of the problem. Each output layer neuron s fully connected to the hdden layer and computes a lnear weghted sum of the outputs of the hdden neurons as follows: (4) n z = y w, j=,,, m j j = 0 where w j s the weght between the -th hdden layer neuron and the j -th output layer neuron. current sample. Wthout pror knowledge, the KNN classfer usually apples the Eucldean dstance as the dstance metrc. Gven two vector x= ( x, x,, x m ) and y= ( y, y,, y m ), ther Eucldean dstance s defned as m (5) = d( x, y) = ( x y ) 6.3 SVM Support vector machnes (SVM) s a relatvely new machne learnng algorthm developed by Vapnk [33]. Based on the statstcal learnng theory of structural rsk management, SVM ams to transform the nput vectors to a hgher dmensonal space by a nonlnear transform, and then an optmal hyperplane whch separates the data can be found. Gven the tranng data set ( x, y ),...,( x, y ), y,, to fnd the optmal l l { } hyperplane, a nonlnear transform, Z =Φ ( x), s used to make tranng data become lnearly dvdable. A weght w and offset b satsfyng the followng crtera wll be found: w z+ b, y = w z+ b, y = (6) he above procedure can be summarzed to the followng: mn Φ ( w) = ( w w) (7) w, b Fg.4 he basc framework of RBFNN 6. KNN K-nearest neghbor (KNN) s an nstance-based classfcaton method, frstly ntroduced by Cover and Hart [3]. KNN has proved popular wth facal expresson recognton due to ts relatve smplcty and performance comparable to other methods. he KNN classfcaton algorthm tres to fnd the K nearest neghbors of the current sample and uses a majorty vote to determne the class label of the subject to y ( w z + b), =,,..., n If the sample data s not lnearly dvdable, the followng functon should be mnmzed. l Φ ( w) = w w+ C ξ (8) = whereas ξ can be understood as the error of the classfcaton and C s the penalty parameter for ths term. By usng the Lagrange method, the decson l functon of w0 = λ y z wll be = E-ISSN: Issue 8, Volume, August 0

7 l f = sgn[ λ y ( z z ) + b] (9) = 0 From the functonal theory, a non-negatve symmetrcal functon K( u, v) unquely defnes a Hlbert space H, where K s the rebuld kernel n the space H : K( u, v) = αϕ ( u) ϕ ( v) (0) hs stands for an nternal product of a characterstc space: z z=φ( x ) Φ ( x) = K( x, x) () hen the decson functon can be wrtten as: l f = sgn[ λ yk ( x, x) + b] () = he development of a SVM classfcaton model depends on the selecton of kernel functon. here are four typcal kernels that can be used n SVM models. hese nclude lnear, polynomal, radal bass functon (RBF) and sgmod functon, as descrbed below. he lnear kernel functon s defned as K( x, x ) = x x (3) j j he polynomal kernel functon s defned as K( x, x ) ( x x coeffcent) degree j = γ j+ (4) he RBF kernel functon s defned as j = γ j (5) K( x, x ) exp( x x he sgmod kernel functon s defned as K( x, x ) = tanh( γ x x + coeffcent) (6) j j Many real-world data sets nvolve mult-class problem. Snce SVM s nherently bnary classfers, the bnary SVM s needed to extend to be mult-class SVM for mult-class problem. Currently, there are two types of approaches for buldng mult-class SVM. One s the sngle machne approach, whch attempts to construct mult-class SVM by solvng a sngle optmzaton problem. he other s the dvde and conquer approach, whch decomposes the mult-class problem nto several bnary sub-problems, and bulds a standard SVM for each. he most popular decomposng strategy s probably the oneaganst-all. he one-aganst-all approach conssts of buldng one SVM per class and ams to dstngush the samples n a sngle class from the samples n all remanng classes. Another popular decomposng strategy s the one-aganst-one. he one-aganst-one approach bulds one SVM for each par of classes. When appled to a test pont, each classfcaton gves one vote to the wnnng class and the pont s labeled wth the class havng most votes. In practce, the one-aganst-one approach s more effectve than the one-aganst-all approach due to ts computaton smplcty and comparable performance. 7 Experments In ths secton, we perform facal expresson recognton experments on two popular facal databases,.e., the JAFFE database and the Cohn- Kanade database, and present expermental results and analyss. 7. Experment setup For the extracton of the Gabor wavelets representaton and LBP, the pre-processng procedure of facal mages s gven as follows. Followng the settng n [, 4], we normalzed the eye dstance of face mages to a fxed dstance of 55 pxels once the centers of two eyes were located. Generally, t s observed that the wdth of a face s roughly two tmes of the dstance, and the heght s roughly three tmes. herefore, based on the normalzed value of the eye dstance, a reszed mage of 0 50 pxels was cropped from orgnal mage. o locate the centers of two eyes, automatc face regstraton was performed by usng a robust realtme face detector based on a set of rectangle Harrwavelet features [34]. From the results of automatc face detecton ncludng face locaton, face wdth and face heght, two square boundng boxes for left eye and rght eye were automatcally constructed by usng the geometry of a typcal up-rght face, whch has been wdely utlzed to fnd a proper spatal arrangement of facal features [35]. hen, the approxmate center locatons of two eyes can be automatcally worked out n terms of the centers of two square boundng boxes for left eye and rght eye. E-ISSN: Issue 8, Volume, August 0

8 Fg.5 shows the detaled process of two eyes locaton and the fnal cropped mage from the Cohn- Kanade database. No further algnment of facal features such as algnment of mouth was performed. Addtonally, there was no attempt made to remove llumnaton changes due to LBP s gray-scale nvarance. he cropped facal mages of 0 50 pxels contan facal man components such as mouth, eyes, brows and noses. he LBP operator s appled to the whole regon of the cropped facal mages. For better unform-lbp feature extracton, two parameters,.e., the LBP operator and the number of regons dvded, need to be optmzed. Smlar to the settng n [, u 4], we selected the 59-bn operator LBP, and P, R dvded the 0 50 pxels face mages nto 8 pxels regons, gvng a good trade-off between recognton performance and feature vector length. hus face mages were dvded nto 4 (6 7) regons, and represented by the LBP hstograms wth the length of 478 (59 4). Fg.5 (a) wo eyes locaton of an orgnal mage from the Cohn-Kanade database, (b) he fnal cropped mage of 0 50 pxels. o reduce the feature length of the Gabor wavelets representatons, prncpal component analyss (PCA) [36] s used for dmensonalty reducton. he reduced feature dmenson s confned to the range of [0, 00] wth an nterval of 0. A 0-fold cross valdaton scheme s employed n 7-class facal expresson recognton experments, and the average recognton results are reported. In detal, the data sets are splt randomly nto ten groups of roughly equal numbers of subjects. Nne groups are used as the tranng data to tran a classfer, whle the remanng group s used as the testng data. he above process s repeated ten tmes for each group n turn to be omtted from the tranng process. Fnally, the average recognton results on the testng data are reported. As a representaton ANN, RBFNN s used for ts computatonal smplcty. For the KNN classfer, we set K to be for ts satsfyng performance. We employ the LIBSVM package, avalable at /~cjln/lbsvm, to perform the SVM algorthm wth the lnear kernel functon, one-aganst-one for mult-class problems. he experment platform s Intel CPU.0 GHz, G RAM memory, MALAB 7.0. (R4). 7. Experments on the JAFFE database When usng the LBP features for facal expresson recognton, the recognton results of dfferent classfcaton methods on the JAFFE database, ncludng ANN, KNN, SVM and SRC, are gven n able. It can be seen from able that SRC obtans the hghest accuracy of 84.76%, outperformng the other used methods. he recognton accuraces usng the extracted LBP features for the other used methods, are 68.09% for ANN, 80.95% for KNN and 79.88% for SVM. When usng the Gabor wavelets representatons for facal expresson recognton, the recognton results of dfferent classfcaton methods along wth reduced dmenson of the Gabor wavelets representatons are presented n Fg.6. able gves the best accuracy of dfferent classfcaton methods wth the correspondng reduced dmenson of the Gabor wavelets representatons. he results n able and Fg.6 reveal that SRC acheves an accuracy of 88.57% at best wth 60 reduced dmenson of the Gabor wavelets representatons, outperformng the other used methods. hs confrms the valdty and hgh performance of SRC for facal expresson recognton. able Comparson of recognton results for dfferent classfcaton methods wth the LBP features on the JAFFE database Methods Accuracy ANN KNN SVM SRC E-ISSN: Issue 8, Volume, August 0

9 Recognton accuracy / % Reduced dmenson ANN KNN SVM SRC Fg.6 Recognton results on the JAFFE database for dfferent classfcaton methods wth the reduced dmenson of the Gabor wavelets representatons able Best results on the JAFFE database for dfferent methods wth correspondng reduced dmenson of the Gabor wavelets representatons Methods Dmenson Accuracy ANN KNN SVM SRC o further explore the recognton accuracy per expresson, able 3 and 4 separately present the confuson matrx of 7-class facal expresson recognton results wth the LBP features and the Gabor wavelets representatons. he bold values n able 3 and 4 represent the recognton accuracy of each expresson. From able 3 and 4, we can observe that three expressons,.e., anger, joy and neutral, are classfed wth an accuracy of around 90%, whle other four expressons, sad, surprse, dsgust and fear, are dscrmnated wth relatvely low accuracy (less than 90%). In our work, the obtaned recognton accuracy of SRC (.e., 84.76% wth the LBP features, and 88.57% wth the Gabor wavelets representatons) for 7-class facal expresson recognton on the JAFFE database s hghly compettve, compared wth prevously reported results on the JAFFE database. In [], smlar to our expermental settngs, they reported the best accuracy of 8% wth SVM and LBP features. In [37], they extracted the local texture nformaton by applyng LBP to facal feature ponts and obtaned an accuracy of 83%.wth the nearest neghbour classfer. In [6], by usng the Gabor wavelets representatons and learnng vector quantzaton (LVQ), they acheved an accuracy of 87.5%. In our prevously publshed work [4], based on LBP and local Fsher dscrmnant analyss (LFDA), we obtaned the best recognton accuracy of 90.7%, outperformng the reported accuracy n ths work. Nevertheless, LFDA s used to extract the low-dmensonal dscrmnatve embedded data representatons from the extracted hgh-dmensonal LBP features wth strkng performance mprovement on facal expresson recognton tasks. 7.3 Experments on the Cohn-Kanade database able 5 presents the recognton results of dfferent classfcaton methods wth the LBP features on the Cohn-Kanade database. Fg.7 gves the recognton results of dfferent classfcaton methods wth the reduced dmenson of the Gabor wavelets representatons. able 6 provdes the best accuracy for dfferent classfcaton methods wth the correspondng reduced dmenson. As shown n Fg.7 and able 5-6, we can see that SRC stll performs best among all used methods for facal expresson recognton. In detal, SRC gves an accuracy of 97.4% wth the LBP features, and 98.09% wth 50 reduced dmenson of the Gabor wavelets representatons. hs ndcates the effectveness of SRC for facal expresson recognton, agan. able 3 Confuson matrx of 7-class facal expresson results wth the LBP features on the JAFFE database Anger Joy Sad Surprse Dsgust Fear Neutral Anger E-ISSN: Issue 8, Volume, August 0

10 Joy Sad Surprse Dsgust Fear Neutral able 4 Confuson matrx of 7-class facal expresson results wth the Gabor wavelets representatons on the JAFFE database Anger Joy Sad Surprse Dsgust Fear Neutral Anger Joy Sad Surprse Dsgust Fear Neutral able 5 Comparson of recognton results for dfferent classfcaton methods wth the LBP features on the Cohn-Kanade database Methods Accuracy ANN ANN KNN SVM SRC KNN 96. SVM 95.4 SRC 97.4 able 6 Best results on the Cohn-Kanade database for dfferent methods wth correspondng reduced dmenson of the Gabor wavelets representatons Methods Dmenson Accuracy E-ISSN: Issue 8, Volume, August 0

11 Recognton accuracy / % Reduced dmenson ANN KNN SVM SRC Fg.7 Recognton results on the Cohn-Kanade database for dfferent classfcaton methods wth the reduced dmenson of the Gabor wavelets representatons able 7 and 8 presents the confuson matrx of 7- class expresson recognton results wth the LBP features and the Gabor wavelets representatons, respectvely. As shown n able 7-8, t can be seen that most of seven expressons are dentfed very well wth an accuracy of 00%. Now we compare our reported results (.e., 97.4% wth the LBP features, and 98.09% wth the Gabor wavelets representatons) wth prevously reported results on the Cohn-Kanade database. In [], they obtaned a 7-class recognton accuracy of 9.4% at best wth LBP features and SVM. In [38], they obtaned the hghest accuracy of 93.4% wth SVM on 7-class tasks, but they used an mproved LBP features called local drectonal pattern (LDP). able 7 Confuson matrx of 7-class facal expresson results wth the LBP features on the Cohn-Kanade database Anger Joy Sad Surprse Dsgust Fear Neutral Anger Joy Sad Surprse Dsgust Fear Neutral able 8 Confuson matrx of 7-class facal expresson results wth the Gabor wavelets representatons on the Cohn-Kanade database Anger Joy Sad Surprse Dsgust Fear Neutral Anger Joy Sad Surprse E-ISSN: Issue 8, Volume, August 0

12 Dsgust Fear Neutral Conclusons Automatc facal expresson recognton has ncreasngly attracted attenton duo to ts mportant applcatons to human computer nteracton. Desgnng a good classfer s a crucal step for any successful facal expresson recognton system. In ths paper, we presented a new method of facal expresson recognton va the sparse representaton classfer (SRC). he experment results on the JAFFE database and the Cohn-Kanade database show that the SRC method obtans the promsng performance on facal expresson recognton tasks due to ts good classfcaton property. In our future work, t s an nterestng task to employ the SRC technque to develop a real-tme facal expresson recognton system for natural human-computer nteracton. In addton, t s also nterestng to nvestgate the performance of the SRC technque to predct the behavor of fnancal tme seres [39]. Acknowledgments hs work s supported by Natonal Natural Scence Foundaton of Chna under Grant No and No.676, and Zhejang Provncal Natural Scence Foundaton of Chna under Grant No.Z0048 and No.Y058. References: [] R Cowe, E Douglas-Cowe, N sapatsouls et al., Emoton recognton n human-computer nteracton, IEEE Sgnal Processng Magazne, Vol. 8, No., 00, pp [] E Hudlcka, o feel or not to feel: he role of affect n human-computer nteracton, Internatonal Journal of Human-Computer Studes, Vol. 59, No. -, 003, pp. -3. [3] M Kurematsu, J Hakura, and H Fujta, A Framework of a Speech Communcaton System wth Emoton Processng, WSEAS ransactons on Systems and Control, Vol. 3, No. 6, 007, pp [4] Y an, Kanade, and J F Cohn, Facal Expresson Recognton, Handbook of face recognton, 0, pp [5] W Zheng, X Zhou, C Zou et al., Facal expresson recognton usng kernel canoncal correlaton analyss (KCCA), IEEE ransactons on Neural Networks, Vol. 7, No., 006, pp [6] S Bashyal, and G Venayagamoorthy, Recognton of facal expressons usng Gabor wavelets and learnng vector quantzaton, Engneerng Applcatons of Artfcal Intellgence, Vol., No. 7, 008, pp [7] B Kara, N Watsuj, Usng Wavelets for exture Classfcaton, WSEAS ransactons on Computers, 003, pp [8] M J Lyons, J Budynek, and S Akamatsu, Automatc classfcaton of sngle facal mages, IEEE ransactons on Pattern Analyss and Machne Intellgence, Vol., No., 999, pp [9] M A urk, and A P Pentland, Face recognton usng egenfaces, Proc. IEEE Conference on Computer Vson and Pattern Recognton (CVPR), 99, pp [0] P N Belhumeur, J P Hespanha, and D J Kregman, Egenfaces vs. fsherfaces: recognton usng class specfc lnear projecton, IEEE ransactons on Pattern Analyss and Machne Intellgence, Vol. 9, No. 7, 997, pp [] Ojala, M Petk nen, and M enp, Multresoluton gray scale and rotaton nvarant texture analyss wth local bnary patterns, IEEE ransactons on Pattern Analyss and Machne Intellgence, Vol. 4, No. 7, 00, pp [] C Shan, S Gong, and P McOwan, Facal expresson recognton based on Local Bnary Patterns: A comprehensve study, Image and Vson Computng, Vol. 7, No. 6, 009, pp [3] X Zhao, S Zhang, Facal expresson recognton usng local bnary patterns and dscrmnant kernel locally lnear embeddng, EURASIP Journal on Advances n Sgnal Processng, 0, 0. [4] S Zhang, X Zhao, and B Le, Facal Expresson Recognton Based on Local Bnary Patterns and Local Fsher Dscrmnant Analyss, E-ISSN: Issue 8, Volume, August 0

13 WSEAS RANSACIONS on Sgnal Processng, Vol. 8, No., 0, pp. -3. [5] Y an, Kanade, and J Cohn, Recognzng acton unts for facal expresson analyss, IEEE ransactons on Pattern Analyss and Machne Intellgence, Vol. 3, No., 00, pp [6] N Sebe, M S Lew, Y Sun et al., Authentc facal expresson analyss, Image and Vson Computng, Vol. 5, No., 007, pp [7] M Bartlett, G Lttlewort, M Frank et al., Recognzng facal expresson: machne learnng and applcaton to spontaneous behavor, Proc. IEEE Conference on Computer Vson and Pattern Recognton (CVPR'05), 005, pp [8] H Meng, and N Banch-Berthouze, "Naturalstc affectve expresson classfcaton by a mult-stage approach based on hdden Markov models," Affectve Computng and Intellgent Interacton, Lecture Notes n Computer Scence, pp : Sprnger, 0. [9] F Dornaka, E Lazkano, and B Serra, Improvng dynamc facal expresson recognton wth feature subset selecton, Pattern Recognton Letters, Vol. 3, No. 5, 0, pp [0] I Kotsa, and I Ptas, Facal expresson recognton n mage sequences usng geometrc deformaton features and support vector machnes, IEEE ransactons on Image Processng, Vol. 6, No., 007, pp [] D L Donoho, Compressed sensng, IEEE ransactons on Informaton heory, Vol. 5, No. 4, 006, pp [] R G Baranuk, Compressve sensng [lecture notes], IEEE Sgnal Processng Magazne, Vol. 4, No. 4, 007, pp. 8-. [3] E J Candes, and M B Wakn, An ntroducton to compressve samplng, IEEE Sgnal Processng Magazne, Vol. 5, No., 008, pp [4] J Wrght, A Y Yang, A Ganesh et al., Robust face recognton va sparse representaton, IEEE ransactons on Pattern Analyss and Machne Intellgence, Vol. 3, No., 009, pp [5] J Wrght, Y Ma, J Maral et al., Sparse representaton for computer vson and pattern recognton, Proceedngs of the IEEE, Vol. 98, No. 6, 00, pp [6] A Wagner, J Wrght, A Ganesh et al., owards a Practcal Face Recognton System: Robust Algnment and Illumnaton by Sparse Representaton, IEEE ransactons on Pattern Analyss and Machne Intellgence, No. 99, 0, pp. -5. [7] Kanade, Y an, and J Cohn, Comprehensve database for facal expresson analyss, Proc. Internatonal Conference on Face and Gesture Recognton, 000, pp [8] E J Candes, and ao, Decodng by lnear programmng, IEEE ransactons on Informaton heory, Vol. 5, No., 005, pp [9] G Donato, M Bartlett, J Hager et al., Classfyng facal actons, IEEE ransactons on Pattern Analyss and Machne Intellgence, Vol., No. 0, 999, pp [30] J Park, and I W Sandberg, Unversal approxmaton usng radal-bass-functon networks, Neural computaton, Vol. 3, No., 99, pp [3] S Zhang, X Zhao, B Le, Spoken emoton recognton usng radal bass functon neural network, CSEE0-Part, Communcatons n Computer and Informaton Scence (CCIS), Sprnger, 4, 0, pp [3] Cover, and P Hart, Nearest neghbor pattern classfcaton, IEEE ransactons on Informaton heory, Vol. 3, No., 967, pp. -7. [33] V Vapnk, he nature of statstcal learnng theory: Sprnger-Verlag, New.York, 000. [34] P Vola, and M Jones, Robust real-tme face detecton, Internatonal Journal of Computer Vson, Vol. 57, No., 004, pp [35] P Campadell, R Lanzarott, G Lpor et al., Face and facal feature localzaton, Proc. Internatonal Conference on Image Analyss and Processng, 005, pp [36] I Jollffe, Prncpal component analyss, Second edton ed., New York: Sprnger, 986. [37] X Feng, B Lv, Z L et al., A Novel Feature Extracton Method for Facal Expresson Recognton, Proc. Jont Conference on Informaton Scences, 006, pp. [38] Jabd, M H Kabr, and O Chae, Robust Facal Expresson Recognton Based on Local Drectonal Pattern, ERI journal, Vol. 3, No. 5, 00, pp [39] F Ner, Agent Based Modelng Under Partal and Full Knowledge Learnng Settngs to Smulate Fnancal Markets", AI Communcatons, 5 (4), IOS Press, 0, pp E-ISSN: Issue 8, Volume, August 0