FACE RECOGNITION USING MAP DISCRIMINANT ON YCBCR COLOR SPACE

Transcription

1 FAC RCOGNIION USING MAP DISCRIMINAN ON YCBCR COLOR SPAC I Gede Pasek Suta Wjaya lectrcal ngneerng Department, ngneerng Faculty, Mataram Unversty. Jl. Majapaht 62 Mataram, West Nusa enggara, Indonesa. mal: gpsutawjaya@te.ftunram.ac.d Abstrak hs paper presents face recognton usng maxmum a posteror (MAP) dscrmnant on YCbCr color space. he YCbCr color space s consdered n order to cover the skn nformaton of face mage on the recognton process. he proposed method s employed to mprove the recognton rate and equal error rate (R) of the gray scale based face recognton. In ths case, the face features vector consstng of small part of domnant frequency elements whch s extracted by non-blockng DC s mplemented as dmensonal reducton of the raw face mages. he matchng process between the query face features and the traned face features s performed usng maxmum a posteror (MAP) dscrmnant. From the expermental results on data from four face databases contanng 2268 mages wth 196 classes show that the face recognton YCbCr color space provde better recognton rate and lesser R than those of gray scale based face recognton whch mprove the frst rank of grayscale based method result by about 4%. However, t requres three tmes more computaton tme than that of grayscale based method. Key Words : MAP, YCbCr color space, PCA, LDA, and face recognton. 1. Introducton Human face recognton s an actve research area n mage processng applcatons because there are many potental applcatons, whch cover human computer nteractons, forenscs, survellance, and securty systems. he publshed approaches mostly related to our system are frequency analyss-based face recogntons, whch are combned wth PCA and LDA as descrbed n Refs. [1-4]. However, they have to retran all face mage classes to get optmal projecton when new classes are added nto the system. Moreover, those systems work n grayscale doman. hs research proposes a face recognton usng maxmum a posteror (MAP) dscrmnant on YCbCr color space. he YCbCr color space s consdered n order to cover the skn nformaton of face mage on the recognton process. In addton, t s employed to mprove the recognton rate and equal error rate (R) of the gray scale based face recognton[5]. he chromnance components (Cb and Cr) have to be consdered n the face recognton because the face mage mostly covers wth skn and skn-tone color whch can be modeled by the chromnance elements. Moreover, the chromnance components of skn-tone color are nonlnearly dependent on the lumnance [6]. hs paper s organzed as follows: secton 2 descrbes the most related work to the proposed method; secton 3 explans the algorthm of classcal LDA-based face recognton and ts weakness; secton 4 explans the MAP dscrmnant and ts advantages; secton 5 explans how to mplement the frequency analyss n the face mage n order to obtan the face features; secton 6 descrbes the recognton algorthm; secton 7 presents the expermental results and compares our results to LDA and egen-face method and also descrbes the results dscusson, and the rest concludes the paper. 2. Related Work he most related approach to our system s face recognton based on LDA and ts varatons as descrbed n Ref. [1-5]. Ref. [1] presented PCA and LDA algorthm n DC doman. he DC was mplemented to reduce data dmensonal of face mage[2]. In addton, the Ref. [2] mplemented the combnaton of DC analyss and face localzaton technques for fndng the global nformaton of face mage, but t requres eyes coordnate whch have to nput manually to perform geometrcal normalzaton. Jurusan eknk lektro UNRAM-Mataram, 17 Jul

2 Ref. [3] mplemented the wavelet transforms to reduce the dmenson of face mage, employed a regulaton scheme for the wthn-scatter matrx, and used optmzaton procedure. It was reported that the Daubeches (Db6) was mplemented to flter mage to resoluton 29 x 23. Ref. [4] proposed Drect LDA (DLDA) to overcome the sngularty problem LDA. However, those approaches stll need hgh memory space and must be retraned when new face class s added. he Ref. [5] proposed the modfcaton of LDA for solvng the retranng problem of LDA, but t just worked on the grayscale face mages. In ths method, we consder the chromnance component of face mages on recognton process and also mplement the frequency analyss (.e. DC) for reducng the orgnal data dmensonal. In addton, the MAP dscrmnant s employed for classfyng the face class wthout localzaton and boundng box processng. 3. LDA for Face Recognton he man purpose of LDA analyss s to fnd a lnear transformaton such that feature clusters are most separable after the transformaton. It can be acheved by the between-class scatter matrx S b and the wthn-class scatter matrx S w analyss, as explaned n Ref. [1,5]. he class separaton s measured by the rato of determnant of the S b matrx to the S w matrx usng the equaton below. 146 arg max S S b w (1) Where =e 1, e 2, e 3,, e m s set of egen-vectors correspondng to m largest egen-values whch satsfy the equaton: Sbe SW e, = 1, 2, 3,, m. he egen-vectors and egen-values are obtaned by computng the nverse of S b and then solvng the egen problem of matrx. Fnally, the projecton S 1 W Sb of the lnear dscrmnant functons s gven by: Y ( C ) C m (2) he ntrnsc problem of the LDA algorthm s the sngularty problem of scatter matrx due to the hgh data dmensonal and small number of tranng samples. Some methods have been proposed to solve that problem as descrbed n Ref. [1,4]. However, those methods can only reduce the large computatonal load and memory space requrement, but the retranng problem s not covered yet. 4. MAP Dscrmnant Analyss In order to avod retranng problem, the MAP s consdered whch s based on assumpton that the matrx scatter has small dmenson and the covarance of the tranng mages s multvarate normal dstrbuton. Suppose we have a set of face tranng contanng n classes wth each class consstng m column vectors of face features. From ths set, mean of each class s easly determned and then placed t nto a mean matrx as = 1, 2, 3,, c. Fnally, we can determne the global covarance usng the wth-n class (S w ) equaton. S w C g c 1 x j C x x 146 Jurusan eknk lektro UNRAM-Mataram, 17 Jul 2012 j j (3) If C g s multvarate normal dstrbuton, we can classfy each face feature to person s class usng the equaton below. F max g (x),g (x),g (x),...,g (x) (4) c Where g (x) s gven by: g ( x) C 1 x 0.5 C m 1 (5) he equaton (5) s derved from maxmum a posteror (MAP) dscrmnant, as descrbed below: g( x) ( 2 g ( x ) P( 1 1 n / 2 1/ 2 )C P( x )P( ) x ) P( x ) exp1/ 2( x ) C ( x ) 1 P( ) P( x) (6) (7) where P(x) s total probablty of x. By assumng all classes have the same covarance and pror probablty, the equaton (5) can be smplfed as below: xc x C x 1 C 2 2 g ( x ) (8) By keepng just the terms dependent on and C, the equaton (5) s obtaned. hs algorthm has some advantages for classfyng the face mages classes: 1. It s smple because t does not requre the egenvalues and egen-vectors. 2. It can solve the retranng problem. It can be llustrated: frstly, when a new class added nto the system, the MAP just calculates the mean and the covarance of ts class; secondly, the newest mean s placed nto the matrx M; and fnally, the prevous covarance s updated by addng t wth the newest class covarance. 3. he computaton complexty s much less than that of the PCA and LDA because of not requrng egen analyss. he weakness of ths classfcaton algorthm s sngularty problem due to hgh data dmensonal and small number of tranng samples. o overcome t, we mplement frequency analyss to decrease the orgnal data dmensonal as explaned n the next secton.

3 In order to deal wth the color nformaton of face mage, those processes are accessed three tmes whch correspond to YCbCr color space then the decson rule s performed by maxmum-mean dstance whch s a modfcaton of equaton (4). Clr F max g1(x),g 2(x),g (x),...,gm(x) (9) c 3 where g ( x ) s mean of ( g ( xy ), g ( xcb ) g ( xcr ) ). 5. Face Features xtracton, In ths research, we employ a holstc approach for face feature extracton based on frequency analyss of an entre face. Our approach s dfferent from the most closely related approach presented n Ref. [1,2]. Ref. [1] mplemented 2D-DC for blocked mage as performed n JPG compresson. Ref [2] mplemented 2D-DC for an entre normalzed face and kept a small number of the DC coeffcents, havng large magntudes to represents face features. Ref [3] mplemented wavelet sub band to create face features. However, our approach mplements a 1- dmensonal DC analyss n the entre mage wthout geometrcal normalzaton and boundng process. In order to generate frequency-matrx of the face mage n DC doman, the 1D-DC algorthms s mplemented twce: on the rows and the columns of the face mage matrx. he pseudo code of DC decomposton of the face mage s shown below: func dctdecompose(i:array[0 r-1, 0.. c-1]) for row = 1 to r do F[row, 0.. c-1]=1d-dc(i[row, 0.. c-1]) end for for col = 1 to c do F[0.. r-1,col]=1d-dc(f[0.. r-1,col]) end for return F end func Where I s face mage matrx and F s DC doman of frequency matrx. From the F, the compact and meanngful face features descrbng holstc nformaton of face mage s created by three steps: frst, convert the frequency doman matrx to a vector usng row orderng technque; second, sort the vector descendng order usng quck sort algorthm, fnally select a small number of vector elements whch have large magntude value (.e., less then 100 elements). 6. he Proposed algorthm here are three man processes n the proposed method: face features extracton, tranng, and recognton processes. he face features extracton unt conssts of color space transformaton, equalzaton, and frequency analyss. Actually, there are two man functons of t: frstly, to decrease the effect of non-unform lghtng condton on the face mage, whch s performed usng standard equalzaton, secondly, to create compact face features usng frequency analyss (.e. usng DC analyss). In ths case, we do not apply blocked DC as performed n JPG compresson but non-blocked DC. In addton, wavelet analyss also can be used to extract the face features. he wavelet analyss usually performs the face analyss usng two bases functon (DB4). However, the DC based features extracton s employed for creatng the face features, because of much energy compactness. hose processes are performed on both tranng and query (probe) face mages. However, n the tranng process, those are performed one tme. Fnally, face classfcaton s done usng MAP dscrmnant. 7. xperment and Results he experments are carred out usng data from fve face databases: IS-Lab. Kumamoto Unversty database, INDIA database, -UNRAM database, ORL face database, and cropped face databases. he IS-Lab database conssts of 48 people and each person has 10 pose orentatons as shown n Fg. 2. he face mages were taken by Konca Mnolta camera seres VIVID 900 under varyng lghtng condton. he -UNRAM database conssts of 40 people and each person has 8 pose orentatons: lookng front, lookng left about 30 0, lookng rght about 30 0, lookng up, lookng down, and wearng accessory such as glasses. he Indan database conssts 61 people (22 women and 39 men), each person has eleven pose orentatons: lookng front, lookng left, lookng rght, lookng up, lookng up towards left, lookng up towards rght, and lookng down. Indan database also ncluded the emotons: neutral, smle, laughter, sad/dsgust. he ORL database was taken at dfferent tmes, under varyng the lghtng condtons, facal wth dfferent expressons (open/closed eyes, smlng/not smlng) and facal detals (glasses/no glasses). All of the mages were taken aganst a dark homogeneous background. he faces of the subjects are n an uprght, frontal poston (wth tolerance for some sde movement). he ORL database s a grayscale face database that conssts of 40 people, manly male. he cropped face databases s open database, conssted of 15 face pose varatons, some of them also ncluded pose accessores such as glasses. he example of face pose of each database s shown n Fg. 1. Jurusan eknk lektro UNRAM-Mataram, 17 Jul

4 (a) (b) In addton, the expermental results shows the MAP based face recognton provde better recognton rate than that of PCA and LDA (see Fg. 2). It can be acheved because the MAP based face recognton performance the classfcaton usng the mahalanobs dstance whle that of PCA and LDA usng ucldean dstance. he second experment was performed to nvestgate the effect of consderng the chromnance component (usng YCbCr color space) of face mage on MAP based face recognton. In ths test, the ORL face database was not ncluded because t s grayscale database. he expermental results show that the features wth consderng the chromnance components of face mage could provde much hgher recognton rate than that of wthout chromnance (grayscale) as shown n Fg. 3. hs achevement means that the chromnance of face mage also contans much dscrmnant nformaton, whch cannot be dscarded from the recognton process (c) Recognton Rate (%) PCA LDA MAP IS -UNRAM INDIA ORL Databases (d) Fg. 2. he recognton rate of the MAP based face recognton method compared wth the most related methods. Recognton Rate (%) Grayscale YCbCr 148 (e) Fg. 1. xample of face poses: (a) IS-Lab., (b) INDIA, (c) UNRAM, (d) ORL and (e) Cropped face databases[5,7-9]. he frst experments was carred out on four face databases: IS-Lab, INDIA, -UNRAM, and ORL. he man am of these tests were to know the performance of MAP based face recognton method compared to PCA and LDA based face recognton. 80 IS INDIA -UNRAM CROP Databases Fg. 3. he recognton rate comparson of the proposed method on both grayscale and YCbCr color space. he thrd experment s performed to nvestgate deeply the effect of chromnance components on 148 Jurusan eknk lektro UNRAM-Mataram, 17 Jul 2012

5 recognton rate. he experments were done on data from IS, INDIA, UNRAM, and Cropped databases consstng of 2268 face mages of 196 classes and the performance evaluaton usng cumulatve matchng score (CMS). he result shows that the chromnance components also gve hgher recognton rate than that of wthout chromnance components. In term of rank one, the chromnance components provde about 4% hgher than that of our prevous method (grayscale), as shown n Fg. 4. It means ths achevement nlne wth the prevous expermental result, whch also prove that the chromnance component of face mage cannot be dscarded from the recognton process. Recognton Rate Grayscale YCbCr Rank Fg. 4. he CMS performance of the proposed method on combne databases. he last experments were done to know the accuracy of the proposed method compared to grayscale based (base lne) performance. he result shows that chromnance components also provde hgher accuracy than that of the base lne (see Fg. 5), whch s ndcated by lower R (about ). It also supports the prevous achevement that the chromnance components provde much useful nformaton for face recognton. False Rejecton Rate False Acceptance Rate Grayscale YCbCr R Fg. 5. ROC of chromnance components on combned database. Regardng to tme consumpton the proposed system requres longer tranng and queryng tmes (almost three tmes of the baselne) because t has to access three tme of the baselne recognton system for processng three-color elements of face mage. However, ths system can solve retranng problem of the LDA and PCA based face recognton, as proved expermentally n the Ref. [5]. he chromnance nformaton tends to provde better result than that of grayscale based recognton because the skn color nformaton of face mage s ncluded n the recognton process. It means that the skn color s mportant nformaton of face mage, whch exst n the chromnance elements. Moreover, human beng can vsually dstngush face mage class usng just skn color. 8. Conclusons he MAP based face recognton could provde better recognton rate than that of PCA and LDA based face recognton wthout requrng the retranng all data samples when new data s added nto the system. he chromnance component (CbCr color space) of face mage contanng much useful dscrmnant nformaton, whch have to be consdered on recognton process. It could mprove sgnfcantly the recognton rate of that of grayscale by about 4% n rank one wth the small enough R (0.0457). However, the color nformaton requred longer processng tme than that of the grayscale. In future, the proposed recognton system wll be tested n large face database (FR) to know further effect of chromnance components on recognton rate. References [1] Chen, W., r, Meng J., and Wu S., PCA and LDA n DC Doman, LSVIR Pattern Recognton Letter, 26, pp , [2] Hafed, Zad M., and Levne, Martn D., Face Recognton Usng the Dscrete Cosne rasnforms, Internatonal Journal of Computer Vson, 43(3), pp , [3] Da, Dao-Qng and Yuen P.C., Wavelet Based Dscrmnant Analyss for Face Recognton, LSVIR Appled Mathematcs and Computaton, 175, pp , [4] Gao H, Davs James W., Why drect LDA s not equvalent to LDA, LSVIR Pattern Recognton Letter, 39, pp , [5] Wjaya, IGPS., Uchmura, K., and Hu, Z. Multpose Face Recognton Based on Frequency Analyss and Modfed LDA, he Journal of IJ, 37(3), pp: , [6] Ren-Len Hsu, Mohamed Abdel-Monttaleb, and Anl K. Jan, Face Detecton n Color Images, I ransactons on Pattern Analyss and Jurusan eknk lektro UNRAM-Mataram, 17 Jul

6 Machne Intellgence, Vol. 24, No. 5, pp , May [7] Samara, F. and Harter, A., Parameterzaton of a stochastc model for human face dentfcaton, 2nd I Workshop on Applcatons of Computer Vson Sarasota (Florda), pp , [8] Database [9] acedatabase.html I Gede Pasek Suta Wjaya receved the B.S. degree n lectrcal ngneerng from Gadjah Mada Unversty n 1997, M.ng. degree n Computer Informatcs System from Gadjah Mada Unversty n 2001, and D.ng from Department of lectrcal ngneerng and Computer Scence, Kumamoto Unversty, Japan. Durng he worked n oyota Astra Motor Company n Indonesa as Plannng Producton Control, and from , next, he worked as lecturer assstance n Yogyakarta Natonal echnology College n Indonesa, and snce 2000 he has been full tme lecturer and has stayed n Informatcs Systems Laboratory n lectrcal Department, Mataram Unversty Indonesa. Snce he got research grant form Natonal ducaton mnstry on mage processng applcaton Jurusan eknk lektro UNRAM-Mataram, 17 Jul 2012