LOCAL BINARY PATTERNS AND ITS VARIANTS FOR FACE RECOGNITION

Transcription

1 IEEE-Internatonal Conferene on Reent Trends n Informaton Tehnology, ICRTIT 211 MIT, Anna Unversty, Chenna. June 3-5, 211 LOCAL BINARY PATTERNS AND ITS VARIANTS FOR FACE RECOGNITION K.Meena #1, Dr.A.Suruland *2 # Assstant Professor, Department of Computer Sene and Engneerng, Sardar Raja ollege Of Engneerng, Trunelvel, Inda. * Assoate Professor, Department of omputer Sene and Engneerng, Manonmanam Sundaranar Unversty, Trunelvel, Inda. 1 meen_nandhu@yahoo.o.n 2 suruland@yahoo.om Abstrat Fae reognton s one of the most mportant tasks n omputer vson and Bometrs. Texture s an mportant spatal feature useful for dentfyng objets or regons of nterest n an mage. Texture based fae reognton s wdely used n many applatons. method s most suessful for fae reognton. It s based on haraterzng the loal mage texture by loal texture patterns. In ths paper performane evaluaton of Loal Bnary Pattern () and ts modfed models Multvarate Loal Bnary Pattern (M), Center Symmetr Loal Bnary Pattern (CS-) and Loal Bnary Pattern Varane (V) are nvestgated. Faal features are extrated and ompared usng K nearest neghbour lassfaton algorthm. G-statsts dstane measure s used for lassfaton. Experments were onduted on JAFFE female, CMU-PIE and FRGC verson2 databases. The results shows that CS- onsstently performs muh better than the remanng other models. Keywords Fae reognton, loal bnary pattern (), Multvarate Loal Bnary Pattern (M), Center Symmetr Loal Bnary Pattern (CS-), Loal Bnary Pattern Varane (V). I. INTRODUCTION Faal reognton plays a vtal rule n human omputer nteraton [4]. A Fae reognton system an be ether verfaton or an dentfaton system dependng on the ontext of an applaton. The verfaton system authentates a person s dentty by omparng the aptured mage wth hs/her own templates stored n the system. It performs a one to one omparson to determne whether the person presentng hmself/herself to the system s the person he/she lams to be. An dentfaton system reognzes a person by hekng the entre template database for a math. It nvolves a one to many searhes. The system wll ether make a math or subsequently dentfy the person or t wll fal to make a math. The human ablty to reognze fae s remarkable. We an reognze the thousands of faes learned throughout our lfetme and dentfy famlar faes at a glane even after years of separaton. Ths skll qute robust, despte large hanges n the vsual stmulus due to vewng ondtons,expressons,agng and dstratons suh as glasses or hanges n harstyle or faal har. Exstng bometr systems are developed for orporate user applatons lke aess ontrol, Computer logon, Survellane amera, Crmnal dentfaton and ATM. Fae reognton system an be grouped as 1.struture based 2.appearane based. In struture based method [12] a set of geometr fae features, suh as eyes, nose, mouth orners, s extrated, the poston of the dfferent faal features form a feature vetor as the nput to a strutural lassfer to dentfy the subjet. In the seond method [2], the appearane of fae as nput to deson makng and they an e further ategorzed as holst and omponent based. The holst appearane methods operate on the global propertes of fae mage. Nowadays,appearane based methods not only operate on the raw mage spae,but also other spaes,suh as wavelet,loal bnary pattern and ordnal pattern spaes. The Loal Bnary Pattern s orgnally proposed by Ojala [7] for the am of texture lassfaton, and then extended for varous felds, nludng fae reognton [9], fae deteton [3], faal expresson reognton [13].The Loal Bnary Pattern s a non parametr operator whh s used for desrbng a loal spatal struture of an mage. The Loal Bnary Patter method s omputatonally smple and rotaton nvarant method for fae reognton [9].Adaptve smoothng for fae mage normalzaton under varaton of llumnaton s presented by Y.K.Park [8]. The llumnaton s estmated by teratvely onvolvng the nput mage wth a 3- by-3 averagng kernel weghted by a smple measure of the llumnaton dsontnuty at eah pxel. In partular, weghts of a kernel are enoded nto a loal bnary pattern () to aheve fast and memory effent proessng. Fae mage s dvded nto several regons and s appled and features are extrated over the regon. These features are onatenated to form fae desrptor [1]. Although fae reognton wth loal bnary pattern has been proven to be a robust algorthm, t suffers from heavy omputatonal load due to the very hgh dmensonal feature vetors that are extrated by onatenatng the hstograms from eah loal regon. A new multhannel flter based Gabor wavelet s desgned based on theory and pratalty. Its enter frequeny s the range from low frequeny to hgh frequeny, ts orentaton s 6 and sale s 6. It an extrat the feature of low qualty faal expresson mage target, and have well robust for automat faal expresson reognton [5] /11/$ IEEE 782

2 IEEE-ICRTIT 211 M s proposed by Aro Lufer [1] for texture segmentaton. Most of the mages are mult band n nature. So ths method s wdely used for mage lassfaton and segmentaton. CS- method was ntrodued by Marko Hekkla [6]. Ths new desrptor has several advantages suh as tolerane to llumnaton hanges, robustness on flat mage areas and omputatonal effeny. varane (V) s proposed by Zhenhua Guo [14] to haraterze the loal ontrast nformaton nto one dmensonal hstogram. In ths paper and ts varants methods are evaluated n JAFFE female database for fae reognton. Among these methods, the best method wll be tested by CMU PIE, FRGC verson2 databases. The rest of the paper s organzed as follows. Seton II revews about and ts dervatves M, CS- and V. Seton III explans about lassfaton prnple. Seton IV reports the expermental data and the results on JAFFE female, CMU-PIE and FRGC verson2 databases. Seton V gves the onluson of ths paper. II. TEXTURE MODELS A. Loal bnary pattern() Loal Bnary Pattern was ntrodued by Tmo ojala [11]. The standard verson of the of a pxel s formed by thresholdng the 3X3 neghborhood of eah pxel value wth the enter pxel s value. Let g be the enter pxel gray level and g (=,1,..7) be the gray level of eah surroundng pxel. Fg.1 llustrate the bas operaton. If g s smaller than g, the bnary result of the pxel s set to otherwse set to 1. All the results are ombned to get 8 bt value. The demal value of the bnary s the feature. the radus and p s the number of neghborhood ponts on the rle.from Fg.2 we an wrte, p 1 1 f x, = ( ) 2, S(x) = otherwse = g p r s g p The onept of unform patterns s ntrodued to redue the number of possble bns. Any pattern s alled as unform f the bnary pattern onssts of at most two btwse transtons from to 1 or ve versa. For example f the bt pattern (no transton) or 11 (two transtons) are unform where as (sx transton) are not unform. The unform pattern onstrant redues the number of pattern from 256 to 58 and t s very useful for fae deteton [1]. B. Multvarate Loal bnary pattern(m) The Multvarate Loal Bnary Pattern operator, M was developed by Aro Lufer [1] whh desrbes loal pxel relatons n three bands. In addton to the spatal nteratons of pxels wthn one band, nteratons between bands are onsdered. Thus, the neghborhood set for a pxel onsst the loal neghbours n all three bands (Fg 3). (1) Fg. 3.M texture measure desrbes spatal relatons wthn a band and between bands Fg. 1 Illustraton of Bas operator Fg.2. The operator of a pxel s rular neghborhoods wth r=1,p=8 Blnear nterpolaton method s used for a samplng pont does not fall n the enter of the pxel. Let p,r denote the feature of a pxel s rularly neghborhoods, where r s M= p 1 = sgng ( sgng ( sgng ( sgng ( sgng ( sgng ( sgng ( sgng ( sgng ( ) (2) From Eqn.2 the loal threshold s taken from these bands, whh makes up a total of nne dfferent ombnatons. Ths results n the followng operator for a loal olor texture desrpton. The olor texture measure s the hstogram of M ourrene, omputed over an mage or a regon of an 783

3 Loal Bnary Patterns and ts Varants for Fae Reognton mage. Ths sngle dstrbuton ontans P 3 2 bns (e.g. P =8 results n 72 bns). C. Center Symmetr Loal bnary pattern(cs-) The CS- s another modfed verson of. It model was developed by Marko Hekkla [6] for the reognton of objet n PASCAL database. The orgnal was very long ts feature s not robust on flat mages. In ths method, nstead of omparng the gray level value of eah pxel wth the enter pxel, the enter symmetr pars of pxels are ompared (Fg.4). CS- s losely related to gradent operator. It onsders the grey level dfferenes between pars of opposte pxels n a neghborhood. So CS- take advantage of both and gradent based features. It also aptures the edges and the salent textures. Fg. 4 CS- feature for a neghbourhood of 8 pxel The CS- features an be omputed by CS N / 2 1,, p r t = 1 f x ( ( /2))2, s(x) = otherwse = t s g g N Where g and g +n/2 orrespond to the gray level of enter symmetr pars of pxels (N n total) equally spaed on a rle of radus r. It also redues the omputatonal omplexty when ompared wth bas [6]. D. Loal bnary pattern varane(v) The V desrptor proposed by Zhenhua [14] offers a better result than. Loal nvarant features have the drawbak of losng global spatal nformaton, whle global features preserve lttle loal texture nformaton. V proposes an alternatve hybrd sheme; globally rotaton nvarant mathng wth loally varant texture features. It s a smplfed but effent jont and ontrast dstrbuton method. p,r /VAR p,r s powerful beause t explots the omplementary nformaton of spatal pattern and loal ontrast. Threshold values are used to quantze the VAR of the test mages omputed to partton the total dstrbuton nto N bns wth an equal number of entres. V where W ( ( k ) = W ( (, N = 1 M j = 1 (, j ), k ) var (, j ), (, j ) = k j ), k ) = (5), otherwse (3) k [, K ] (4) These threshold values are used to quantze the varane of test mages. III. CLASSIFICATION PRINCIPLE A. Tranng In the tranng phase, the texture features are extrated from the samples seleted randomly belongng to eah fae lass, usng the proposed feature extraton algorthm. The average of these features for eah fae lass s stored n the feature lbrary, whh s further used for lassfaton. B. Texture Smlarty To fnd out the smlarty between tranng models and testng sample G-statst dstane measure s used. Smlarty between the textures s evaluated by omparng ther pattern spetrum. The spetrums hstograms are ompared as a test of goodness-of-ft usng a non-parametr statsts, also known as the G-statsts [7].The G statst ompares the two bns of two hstogram and s defned as G = n = f log f s, m 1 n n log = f = s, m n = f log 1. s m s, m n + = f s, m 1 n log = s, m 1 C. Classfaton In the texture lassfaton phase, the texture features are extrated from the test sample x usng the proposed feature extraton algorthm, and then ompared wth model feature usng K-Nearest Neghbor lassfaton algorthm.in experment 1, K=1 s used. (e) mnmum dstane lassfer s used. Mnmum dstane between the model feature value and the sample feature value s alulated. IV. EXPERIMENTS A. Expermental Data The dsrmnaton apablty of any method s done by expermental tests usng the benh mark data base. Fg 5 Sample Images from JAFFE Female database (6 ) Fg. 5 shows the mages from JAFFE female database. It ontans 56 mages. (8 mages X 7 poses=56 mages).all the f f f 784

4 IEEE-ICRTIT 211 tranng and testng mages are pre-proessed to the sze of 12X12. Fg 6. Samples from the CMU-PIE fae database. The frst mage from the left s a sample tranng mage and the others are the sample testng mages. Fg.6 and Fg.7 represents the nput mages for Fg.6 represents the mages from CMU-PIE database. Among these fve mages only one s used for tranng and the remanng four mages are used for testng purpose. the nrease n wndow sze as well as the nrease n the number of samples taken for lassfaton. Our expermental results show that CS- provdes better results than the remanng other methods. TABLE 2 RECOGNITION RATE FOR DIFFERENT NUMBER OF SAMPLES Wndow sze=3x3 SL NO No of Samples Reognton Rate (%) M CS- V Fg 7. Samples from the FRGC Verson2 fae database. The frst mage from the left s a sample tranng mage and the others are the sample testng mages. Smlarly Fg 7 shows the mages from FRGC verson2 database. Here frst mage from the left sde s used for tranng and the remanng mages are used for testng phase. B. Expermental omparsons on JAFFE Female database TABLE 1 RECOGNITION RATE FOR DIFFERENT WINDOW SIZE No of testng Samples=3 SL NO Wndow Sze Reognton Rate (%) M CS- V C. Expermental omparsons on CMU-PIE and FRGC Verson2 databases for llumnaton varatons Experment#3 s onduted on CMU-PIE and FRGC verson2 database whh s shown n Fg.6. The frst mage from the left sde s taken as tranng mage and the remanng four mages are used as testng mages. In tranng phase, faal features are extrated by CS- method and stored n the database. 1 1x x x Experments are onduted on JAFFFE database by varyng the wndow sze and also varyng the number of nput samples from eah mage. Durng experment#1 the number of tranng sample s fxed as 1 and the wndow sze s vared from 1X1. Durng experment#2, the wndow sze s fxed as 3X3 and the number of testng sample s nreased from 1. Table 1 and Table 2 shows that reognton rate nreases wth x1 2x2 3x3 CMU-PIE 785

5 Loal Bnary Patterns and ts Varants for Fae Reognton Durng testng phase, faal features are extrated by usng the above method and the dfferene between two faal features s evaluated by G-statst dstane measure wth k=1(nearest neghbour lassfaton) algorthm. Ths expermental results show that fae reognton s manly depends on llumnaton hanges. TABLE 4 RECOGNITION RATE OF FRGC VERSION2 DATABASE BY CS- METHOD x1 2x2 3x3 FRGC Verson2 TABLE 3 and 4 shows the reognton rate vs wndow sze of CS- method on CMU-PIE and FRGC Verson2 databases. It shows that reognton rate nreases wth nrease n wndow sze. CMU-PIE database gves better results than FRGC Verson2 database under dfferent lghtng ondtons. V. CONCLUSIONS s grey sale nvarant and rotatonal nvarant. Ths property s well sutable for many applatons. The faal reognton based on Loal Bnary Patterns s extremely smple. In ths paper and ts modfed models CS-, M and V were analysed. CS- performs very well and gves the reognton rate of 87% wth the JAFFE female database. CMU-PIE and FRGC Verson2 databases are expermented by the same model under dfferent llumnatons. The model gves the reognton rate of 8% for CMU-PIE and 82% for FRGC Verson2 database. CS- provde good reognton rate than other methods and also t onsumes less omputatonal tme. REFERENCES [1] Arko Lueer, Alfred Sten and Peter Fsher, Multvarate Texturebased Segmentaton of Remotely Sensed Imagery for Extraton of Objets and Ther Unertanty. [2] K.Etemad and R.Chellappa, Dsrmnant Analyss for fae reognton of Human Fae mages, I.Optal So. Am.,Vol.14,PP ,1997. [3] A.Hadd,,M.Petkanan, Tmo Ahonen, A Dsrmnatve Faal feature for detetng and reognzng faes,in CPVR(2),pages ,24. [4] Handbook of Fae Reognton,S.Z L and A.K.Jan,eds.Sprnger,25. [5] M.Lyons,S.Akamatsu,et., Codng Faal Expressons wth Gabor wavelets,proeedng of the thrd IEEE Internatonal onferene on Automat fae and Gensture Reognton,Nero Japan,(1998)2-25. [6] Marko Hekkla,Matt Petkanen, Cordela Shmd, Desrpton of nterest regons wth Loal BnaryPattern, June 28. [7] T.Ojala,M.Petkanan,and D.Hawood, A Comparatve study of texture mages wth lassfaton based on featured dstrbutons,pattern reognton,29(1):51-59,1996. [8] Y.K.Park and J.K.Km, Fast adaptve smoothng based on for fae reognton,electron LETTERS, 22 nd November 27,Vol 43,No 24. [9] Tmo ojala, Matt Petkänen, Top Mäenpää, Multresoluton gray sale and rotaton nvarant Texture Classfaton wth Loal Bnary Patterns IEEE Transatons on pattern analyss and Mahne Intellgene Volume 24 Issue 7, July 22. [1] Tmo Ahonen,Matt Petkanen, Fae Desrpton wth Loal Bnary Patterns: Applaton to Fae Reognton, IEEE Transatons on pattern analyss and Mahne Intellgene Volume 28,No 12,DECEMBER 26. [11] Tmo Ahonen,Matt Petkanen and T.Maenpaa, Multresulaton gray sale and rotaton nvarant texture lassfaton wth loal bnary pattern, IEEE Transatons on pattern analyss and Mahne Intellgene Volume 24,No 7,pp ,22. [12] M.Turk and A.Pentland, Egen faes for fae reognton,j.cogntve Neurosene,Vol 3,no 1,pp.71-86,1991. [13] Zhao and M.Petkanan,Dynam, texture reognton usng Loal Bnary Patterns wth an applaton to faal expressons.ieee Transaton.Pattern Anal.Mah.Intell.,29(6): ,27. [14] Zhenhua Guo,Le Zhang,Davd Zhang, Rotaton Invarant texture lassfaton usng varane (V) wth global mathng. Pattern Reognton 43 (21)