FACIAL FEATURE EXTRACTION TECHNIQUES FOR FACE RECOGNITION

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1 Journal of omputer Scence 10 (12): , 2014 ISSN: Rahb H. Abyev, hs open access artcle s dstrbuted under a reatve ommons Attrbuton (-BY) 3.0 lcense do: /jcssp Publshed Onlne 10 (12) 2014 ( FAIAL FEAURE EXRAION EHNIQUES FOR FAE REOGNIION Rahb H. Abyev Appled Artfcal Intellgence Research entre, Near East Unversty, Ncosa, North yprus, Mersn-10, urkey Receved ; Revsed ; Accepted ABSRA Face recognton s one of the bometrc technques used for dentfcaton of humans. he desgn of the face recognton system ncludes two basc steps. he frst step s the extracton of the mage s features and the second one s the classfcaton of patterns. Feature extractng s a very mportant step n face recognton. he recognton rate of the system depends on the meanngful data extracted from the face mage. If the features belong to the dfferent classes and the dstance between these classes are bgger then these features are mportant for recognton of the mages. In ths study, the desgn of face recognton system usng three dfferent feature extracton technques- Prncpal omponent Analyss (PA), Fsher Lnear Dscrmnant Analyss (FLD) and Fast Pxel Based Matchng (FPBM) s presented. he comparatve analyss of the smulaton results of these methods s presented. Keywords: Face Recognton, PA, FLD, Fast Pxel Based Matchng 1. INRODUION Snce the last century bometrc technques were used for dentfcaton of humans. Faces are one of many forms of bometrcs used to dentfy ndvduals and to verfy ther dentty. Face recognton refers to the automated method of verfyng a match between two human faces. Feature extracton s a very mportant step n face recognton. he recognton rate of system depends on the meanngful data extracted from the face mage. If the features belong to dfferent classes and the dstance between these classes s large then these features are mportant for a gven mage. here s no 100% matchng between the mages of the same face even f they were from the same person. In ths study, the analyss of face recognton systems usng three dfferent feature extracton technques-prncpal omponent Analyss (PA), Fsher Lnear Dscrmnant analyss (FLD) and Fast Pxel Based Matchng (FPBM) s consdered. PA s a technque that takes hgh-dmensonal mage data and uses the dependences between the varables to represent t n a more tractable, lowerdmensonal form, wthout losng too much nformaton. PA s a statstcal procedure that evaluates the covarance structure of a set of varables and dentfes the prncpal drectons n data varables. PA s used to dentfy sets of orthogonal coordnate axes through the data. Prncpal components are determned by computng egenvectors and egenvalues of the data covarance matrx. Based on prncpal components the dentfcaton of face mages s performed. FLD s the most famous way to search for trends n the data, whch has the largest dfference and hghlght data. hs method s also used, for lower-dmensonal representaton of the data, whch removes some of the trends nosy. he basc dea of FLDA s the desgn an optmal transform, whch can maxmze the rato of between-class to wthn-class scatter matrces so that the classes can be well separated n the low-dmensonal space. FLD method allows nformaton between members of the same category (mages of the same person) to develop a set of feature vectors. FLD uses a lnear projecton of the n-dmensonal data onto a one-dmensonal space (.e., lne). Projecton onto a lne s separated by a class and classfcaton problem becomes choosng a lne Scence Publcatons JS

2 Rahb H. Abyev / Journal of omputer Scence 10 (12): , 2014 FPBM s a method to extract the features of the mages on the bass of matchng mage areas and subpxel dsplacement estmate usng smlarty measures. he recognton s based on the edge detecton. hs method generates much less nformaton than the orgnal mage has. hs s because t elmnates most of the detals that are not relevant for the purpose of dentfyng the boundares, whle preservng the essental nformaton to descrbe the shape and structural characterstcs and geometry of the objects represented. hs study descrbes the desgn of a face recognton system usng PA, FLD and FPBM methods. Each of these technques was mplemented n MALAB. he outputs of the feature extracton block are classfed to recognze the face patterns. he algorthm uses Eucldean Dstance for classfcaton of face mages. omparsons of the smulaton results of face recognton systems usng PA, FLD and FPBM algorthms are presented. 2. FEAURE EXRAION EHNIQUES 2.1. Prncpal omponent Analyss In the result of applcaton of the PA algorthm an orgnal data of mage s projected nto a new coordnate space. Each coordnate axs n the new coordnate space wll represent a prncpal component vector. he frst prncpal component vector s the drecton along whch the varance s a maxmum; the second prncpal component vector s defned by the drecton whch maxmzes the varance among all drectons orthogonal to the frst vector and so on. PA algorthm ncludes the followng steps (Smth, 2002; Wold et al., 1987; zmropoulos et al., 2011). he frst step s the readng of the face mages from the database and convertng them nto grayscale values. After these operatons obtaned 2D face mages are converted nto 1D mage vector. he mages are converted to represent each face mage of dmensons NxN to sngle beam of dmensons N N to sngle beam of dmensons N 2 x1. he data are stored n the = [ α] vector. Here α s the converted mage represented n 1D, s the vector that contans all converted mages. In the second step the mean of mages of vector s calculated Equaton 1: X 1 m =, = 1,2,..., X (1) X = 1 where, m s a mean, X s a number of mages n the database. In the thrd step the devaton Φ of each mage from the mean mage are determned Equaton 2: Φ = m, = 1,2,..., X (2) In the fourth step the egenvectors of the covarance matrx = A A are calculated. Here A = [Φ 1, Φ 2,..., Φ X ]. In ths step t s necessary to solve the egenvalue problem (urk and Pentland, 1991) Equaton 3: U = UΛ (3) Here Λ s a dagonal matrx that represents the egenvalues of the matrx and Λ = dag[ λ 1, λ 2,, λ NN ]. U s the assocated egenvectors of λ. hese egenvectors represent the new face space. In the ffth step a centred mage vector s projected nto face space Equaton 4: temp = U A P = [ Ptemp] (4) where, P s a vector that contans all projected mages. he orgnal mage vector A may be reconstructed from the projectons: In the sxth step PA features are extracted from the test mages In the seventh step Eucldean dstances are calculated (Sato and orwak, 1994; Danelsson, 1980) Equaton 5: ( temp = norm P P E = E temp 2 [ ( ( ))], [ ] (5) where, E s the Eucldean dstance vector. In the eghth step the mnmum Eucldean dstance usng mn (x) functon s computed. he correspondng ndex wth the mnmum dstance s the recognzed mage Fsher Lnear Dscrmnant Analyss Usng FLD algorthm the calculatons of the wthn scatter matrx and the between scatter matrx are performed to obtan the projected fsher mages that are used n recognton (Wellng, 2005; ucker et al., 1997; Mka et al., 1999). Fsher s lnear dscrmnant functon J s defned usng covarance matrces. FLD consders maxmzng the followng objectve Equaton 6: W SBW J ( w) = (6) W S W W 2361 Scence Publcatons JS

3 Rahb H. Abyev / Journal of omputer Scence 10 (12): , 2014 Where: S B s the between classes scatter matrx and S W s the wthn classes scatter matrx hey are defned as Equaton 7 and 8: S ( )( ) B = N µ X µ X (7) S ( )( ) W = X µ X µ (8) Where Equaton 9 and 10: 1 µ = X N (9) 1 1 X = X = Nµ N N (10) where, N s a number of cases n the class Equaton 11: he total scatter S : S ( )( ) = X X X X (11) Is gven by Equaton 12: S = SW + SB (12) he next step s the elmnatng of egenvalues and sortng all non-zero egenvalues n descendng order as V, where V s FLD egenvector. he computng of the projected mages usng FLD algorthm s performed as Equaton 13: P = V P (13) In next step FLD features are extracted from the test mage. hen Eucldean dstances are calculated as (Sato and orwak, 1994; Danelsson, 1980) Equaton 14: ( temp = norm P P E = etemp 2 [ ( ( ))], [ ] (14) where, E s the Eucldean dstance vector. Fnally the mnmum Eucldean dstance usng mn (x) functon s computed. he correspondng ndex wth the mnmum dstance s the recognzed mage from database folder Fast Pxel Based Matchng usng Edge Detecton he recognton of the contours (edge detecton) s used for the purpose of markng the ponts of a dgtal mage n whch the lght ntensty changes abruptly. Abrupt changes of the propertes of an mage are usually a symptom of events or major changes of the physcal world. hese changes can be dscontnuty of the depth n the surface, changng the propertes of materals and varatons n lghtng condtons from the surroundng envronment. he edge detecton s a research feld of mage processng, partcularly the branch of feature recognton. he operaton of edge detecton generates mages contanng much less nformaton than the orgnal, because t elmnates most of the detals that are not relevant for the purpose of dentfyng the boundares (Marr and Hldreth, 1980; Fraser, 1985). he methods used for edge detectons can be grouped nto two categores: Search-based and zero-crossng based. Search based methods recognze the contours by computng the maxma and the mnma of the frst order dervatve of the mage, usually lookng n the drecton n whch we have the maxmum local gradent. Zero-crossng based methods seek for the zero-crossng ponts at whch the dervatve of the second order passes through zero, usually the Laplacan functon or a dfferental expresson of a non-lnear functon. he contours play a very mportant role n many applcatons of computer vson. A typcal contour could be, for example, the boundary between an area of red colour and a yellow, or a lne wth a thckness of a few pxels and a dfferent colour compared to a unform colour background. he model llustrated here, has a functon error erf that can be used to create a mathematcal model of the effects of the blurs suffcent accurate to descrbe many practcal applcatons. An mage f wth a onedmensonal contour postoned exactly n 0 can be represented then by the followng functon Equaton 15: Ir Il x f ( x) = erf Il 2 2σ alculaton of the Frst Dervatve (15) Many algorthms for the recognton of contours operate on the frst order dervatve of the lght ntenstywhch corresponds to the gradent of the ntensty of the ntal mage. Based on ths we search the peak values of 2362 Scence Publcatons JS

4 Rahb H. Abyev / Journal of omputer Scence 10 (12): , 2014 the gradent of ntensty. If I (x) represent the ntensty of pxel x and I (x) denotes the dervatve (gradent ntensty) to the pxel x, we get Equaton 16: I '( x) = 1/ 2. I ( x 1) + 0. I( x) + 1 / 2. I( x + 1) (16) alculaton of the Second Dervatve Other operator for edge detecton s based on a calculaton of the second order dervatve of the ntensty, whch roughly corresponds to the rate of change of the gradent. In the deal case-n whch the ntensty vares n a contnuous manner-the second dervatve vanshes at the ponts of maxmum gradent. hs method, however, works well only f the mage s represented n a sutable scale. As explaned before, a lne corresponds to a double contour and then you wll have a gradent of ntensty on one sde of the lne, mmedately followed by a gradent of opposte value on the opposte sde. For ths reason t can be expected to have large varatons n the gradent mages contanng lnes. If I (x) s the ntensty value at the pont x and I (x) s the second dervatve at the pont x, then the followng relaton holds Equaton 17: I ''( x) = 1. I( x 1) 2. I( x) + 1. I( x + 1) (17) Nowadays set of operators s appled for edge detecton operaton. Roberts, Prewtt, Sobel operators of the frst order, Marr-Hldreth method based on the second order. urrently the anny algorthms-and ts varants - are the most used method for the recognton of contours. None of the numerous other subsequently proposed methods have so far proved more effectve, except n very specfc applcatons. In hs orgnal work, anny set out to fnd a flter that would elmnate the nose n the mage. he flter could be well approxmated by a Gaussan kernel of the frst order. anny also ntroduced the concept of non-maxmum suppresson, n whch the gradent reaches the maxmum value n the estmated drecton of the gradent. he search for non-maxmum n a grd of ponts can be mplemented by calculatng the gradent drecton wth the frst dervatve, roundng the drecton found n multples of 45 and fnally compared wth the values of ampltude of the gradent n the drecton calculated. 3. SIMULAION RESULS he smulaton s performed on the ORL face database whch contans a set of face mages taken between Aprl 1992 and Aprl 1994 at the Olvett Research Laboratory n ambrdge Unversty ( a/att_faces.zp). For some persons, the mages were taken at dfferent tmes, varyng the lghtng, facal expressons- open, closed eyes, smlng, not smlng and facal detals- glasses and no glasses. Fg. 1 depcts samples of orgnal mages used for recognton. he smulaton of the facal recognton system s performed usng PA, FLD and FBPM feature extracton technques. Some of these methods have been analysed n (han et al., 2010). In ths study we are unfed these three feature extracton methods for dace recognton and gve ther comparatve results. he classfcatons of the face mages are performed by measurng Eucldan dstance. he smulatons are performed n two stages. On the frst stage the nosy mages (Fg. 2), n the second stage the face mages wthout nosy are consdered for recognton (Fg. 3). In the frst experment, 138 mages of 23 persons are taken. In the database each person has 6 dfferent mages. In ths experment the number of test nosy mages s 46 (able 1). Usng PA the 40 mages were recognzed successfully and recognton rate (R.R) was 86.9%. Usng FLD the 42 mages were recognzed successfully and recognton rate was 91.3%. Usng FPBM the 36 mages were recognzed successfully and recognton rate was 78.2%. In the second experment, 231 mages are taken. In the database these mages belong to 33 persons. Each person has 7 dfferent mages. Number of nosy test mages used n ths experment was 99. Usng PA the 91 mages were recognzed successfully wth recognton accuracy 91.9%. Usng FLD the 92 mages were recognzed successfully wth recognton accuracy 92.9%. Usng FPBM the 75 mages were recognzed successfully wth recognton accuracy 75.6%. In the thrd experment, 320 mages are taken. hese mages are belong to 40 persons and each person has 8 dfferent mages. A number of nosy test mages used n ths experment were 80. Usng PA the 71 mages were recognzed successfully wth recognton accuracy 88.7%. Usng FLD the 75 mages were recognzed successfully wth recognton accuracy 93.8%. Usng FPBM the 59 mages were recognzed successfully wth recognton accuracy 73.8%. In the next stage the smulaton results were obtaned usng non-nosy test mages from the second set. able 2 shows the smulaton results of non-nosy face recognton system usng PA FLD and FPBM algorthms Scence Publcatons JS

5 Rahb H. Abyev / Journal of omputer Scence 10 (12): , 2014 In the frst experment, 65 mages of 13 persons are taken. In ths mage database each person has 5 dfferent mages. Number of test mages that are used n ths experment s 65. Usng PA the 59 mages were recognzed successfully wth the recognton rate 90.8%. Usng FLD the 62 mages were recognzed successfully wth the recognton rate 95.4%. Usng FPBM the 45 mages were recognzed successfully wth the recognton rate 69.2%. In the second experment, 135 mages of 27 persons are taken. Each person has 5 dfferent mages. Usng PA the 127 mages were recognzed successfully wth recognton accuracy 94.1%. Usng FLD the 130 mages were recognzed successfully wth recognton accuracy 96.2%. Usng FPBM the 90 mages were recognzed successfully wth recognton accuracy 66.7%. Fg. 1. Samples of orgnal mages Fg. 2. Nosy test mages Fg. 3. est mages wthout nosy able 1. Recognton rates of the system for nosy tested face mages Subjects ested faces Faces n database PA R.R (%) FLD R.R (%) FPBM R.R (%) able 2. Recognton rates of the system for non-nosy tested face mages Subjects ested faces Faces n database PA R.R (%) FLD R.R (%) FPBM R.R (%) Scence Publcatons JS

6 Rahb H. Abyev / Journal of omputer Scence 10 (12): , 2014 In the thrd experment, 200 mages are taken. hese mages belong to 40 persons and each person has 5 dfferent mages. Usng PA the 189 mages were recognzed successfully wth recognton accuracy 94.5%. Usng FLD the 191 mages were recognzed successfully wth recognton accuracy 95.5%. Usng FPBM the 125 mages were recognzed successfully wth recognton accuracy 62.5%. he smulaton results demonstrate the effcency of usng of PA and FLD methods over FPBM method n face recognton. 4. ONLUSION hree dfferent feature extracton methods (PA, FLD and FPBM) are appled for face recognton. Eucldean dstance s used for classfcaton of the face mages. he structure of the face recognton system was desgned and the computer smulaton of the recognton system has been performed for two knds of face mages: Nosy and wthout nosy cases. he smulatons have been done usng dfferent number of mages. he best average recognton rate for nosy and non-nosy mages was obtaned usng FLD method. he recognton rate of 40 mages was obtaned usng FLD method equals to 93.8% and 95.5% for nosy and non-nosy cases respectvely. he performances of the FPBM for the gven mages were 73.8% and 62.5% respectvely. he performances of the PA for the gven mages were 88.7% and 94.5% respectvely. he smulaton results demonstrate that the FLD method s useful for recognton of face mages. 5. REFERENES han, L.H., S.H. Salleh and.m. ng, Face bometrcs based on prncpal component analyss and lnear dscrmnant analyss. J. omput. Sc., 6: DOI: /jcssp Danelsson, P.E., Eucldean dstance mappng. omput. Graphcs Image Proc., 14: Fraser,.M., alculaton of hgher dervatve terms n the one-loop effectve Lagrangan. Zetschrft für Physk Partcles and Felds, 28: DOI: /BF Marr, D. and E. Hldreth, heory of edge detecton. Proc. Royal Soc. London. Seres B., 207: DOI: /rspb Mka, S., G. Ratsch, J. Weston, B. Scholkopf and K.R. Mullers, Fsher dscrmnant analyss wth kernels. Processng of the IEEE Sgnal Processng Socety Workshop Neural Networks for Sgnal Processng IX, (SPSW, 99), IEEE Xplore Press, Madson, WI, pp: DOI: /NNSP Sato,. and J.I. orwak, New algorthms for eucldean dstance transformaton of an-dmensonal dgtzed pcture wth applcatons. Patt. Recognt., 27: DOI: / (94) Smth, L.I., A tutoral on prncpal components analyss. ornell Unversty, USA. ucker, K., S.P. Rushton, R.A. Sanderson, E.B. Martn and J. Blaklock, Modellng brd dstrbutonsa combned GIS and Bayesan rule-based approach. Landscape Ecol., 12: DOI: /BF urk, M.A. and A.P. Pentland, Egenfaces for Recognton. J. ogntve Neuroscence, 3: DOI: /jocn zmropoulos, G., S. Zaferou and M. Pantc, Prncpal component analyss of mage gradent orentatons for face recognton. Proceedngs of the IEEE Internatonal onference on Automatc Face and Gesture Recognton and Workshops, Mar , Santa Barabara, USA. pp: DOI: /FG Wellng, M., Fsher lnear dscrmnant analyss. Department of omputer Scence, Unversty of oronto. Wold, S., K. Esbensen and P. Gelad, Prncpal component analyss. hemometrcs Intellgent Laboratory Syst., 2: DOI: / (87) Scence Publcatons JS