HUMAN RECOGNITION USING BIOMETRIC AUTHENTICATION SYSTEM

Transcription

1 HUMAN RECOGNIION USING BIOMERIC AUHENICAION SYSEM M. Jasmne Pemeena Pryadarsn, K. Murugesan 2, Srnvasa Rao Inbathn and A. Arun Kumar School of Electroncs Engneerng, VI Unversty, Inda 2 Sree Sastha Insttute of Engneerng and echnology, Chenna, Inda E-Mal: asmn@vt.ac.n ABSRAC A model s desgned to work for Face recognton, Fnger Prnt recognton, and Sgnature recognton for recognzng the ndvdual person test mages out of tranng mages. here are varous methods for recognton and mages need to have good sensor qualty. A Fsher LDA approach whch produces a set of Egen faces and fsher faces to obtan proected mages has been mplemented. In ths paper both PCA and LDA technques have been used. Mnutae matchng algorthm whch after several preprocessng stages produces mnutae ponts on fnger prnt has been mplemented. he offlne sgnature s taken for verfcaton and recognton system; Global features are extracted and matched. A set of fsher mages are proected and reconstructed. he test mage s also proected and a mnmum error reconstructon value s calculated. If error s less than a threshold value, then t recognzes the face from the database. A set of false mnuta ponts are extracted and effcently the mnuta ponts are removed from the fnger and made nto a template and verfcaton s done wth other template for producng percentage score of the matchng template. After extractng Global features from the sgnature, the same steps are appled for the nput sgnature and matched wth the database of sgnature mages. Multmodal bometrc authentcaton s appled for verfcaton and dentfcaton of humans where same the human beng database s matched wth the nput mage. Keywords: authentcaton system, LDA, PCA, mnuta, egen faces, fsher faces, global features. INRODUCION he man obectve of ths paper s multmodal bometrc authentcaton. Bometrc systems make use of behavoral trats for recognton. In the bometrcs three dfferent trats are used. Each has a separate method such as Face, Fngerprnt, and Sgnature. A unmodal bometrc system s easly affected by nose, error and attacks. A tranng set of database s taken for an dentfcaton of each bometrc wth the testng database. Each stage has separate algorthm for determnaton of nput mage from the database. BIOMERICS Bometrcs comprses methods for recognzng human dependng upon physcal or behavoral trats. In computer scence ths bometrcs s used as a form of dentty access management and access control. It s also used for fndng an ndvdual person under groups through survellance [4],[ ]. A bometrc system s operated n these two modes: Verfcaton: In ths one to one comparson s done where stored template can be matched wth database of template to verfy to who t s. In ths t s not be possble to match all the template at a tme. Identfcaton: Many comparsons are done by matchng an nput wth a database a bometrc n order to dentfy the unknown ndvdual. Identfcaton succeeds when the ndvdual bometrc matchng wth the database of bometrc falls wth n a prevously set threshold. Bometrc authentcaton Bometrc authentcaton s the process of usng ndvdual physcal or behavoral trat or a method to confrm the dentty and determne the profle of a person. Bometrcs has gently ncreased ts popularty n the data collecton devces. Common examples of bometrc authentcaton nclude fngerprnt scannng and voce actvated locks. here are two types of bometrc authentcaton: physologcal and behavoral. Physologcal bometrcs depends on each unque physcal trat such as fngerprnt, palm prnt, DNA, Irs, or face recognton. In ths type of system, a scan of the trat s taken at a secured ste and connected to profle of person. Securty rghts are assgned to ths profle, based on the person s ob or securty access level. hs nformaton s stored n a secured system connected drectly to the ndvdual locks or securty statons. Behavor authentcaton depends on the actual behavor of the person. Some of the examples of ths type of authentcaton nclude voce, gat, and speakng rhythm or dcton. Where as t s easy to mmc the sound of another person s voce, the actual tone or note of ther speech s harder to duplcate. hs type of securty s most often used to access computer fles or other system mantaned securty. Image acquston s done through dfferent types of sensor for dfferent bometrcs. he frst block acqures necessary nformaton from the sensor whch acts as nterface between the real world and the system. he second block s the preprocessng stage whch removes the artfcal nose or mpulse nose from the sensor to enhance or remove background nose from the data acqured. A 909

2 template s the synthess of characterstc features extracted from a source. Feature extracton stage correct features of an mage are to be extracted n an optmal way then t s matched wth the database or template for recognton of an ndvdual. In the matchng stage, the matchng s performed by obtanng the template from feature vector stage and ths template s matched wth stored templates or database. hs can be calculated by estmatng the dstance between them usng any algorthm lke hammng dstance, Eucldean dstance. hen matchng program f the nput s matched wth database then t may be accepted or reected. ypes of bometrcs Face recognton Face recognton works by usng computer for analyzng a subect s facal structure. Face recognton software takes a number of key measurements such as dstance between eyes, nose and mouth, angles such as aw and forehead, and length of varous portons of face. hen by usng ths key measurement a template s created whch s matched wth enormous database of face mages to dentfy the ndvdual [, 3]. Face recognton must have several problems n dfferent technques. hs dffculty arses because face must be represented n a way that best utlzes the avalable face nformaton to dstngush a partcular face from all other faces. Faces pose a partcularly dffcult problem n ths. Some faces are smlar to one another n that they contan same features such as eyes, nose and mouth arranged n roughly the same manner. Fngerprnt recognton Fngerprnt s unque for each person n the world, even twns also. Fngerprnt recognton software are used for desktop, laptops, cell phones nstead of password whch s more effcent. hs type of fngerprnt devces s avalable for vendors at low cost. Instead of password, ust a touch s enough for nstant access of devce. Several states check fngerprnts for new applcants to socal servce benefts to ensure recpents do not fraud under fake names. Fnger prnts have four types whorl, left loop, rght loop and arch. Crmnals ncluded n bank robberes, murder cases are dentfed wth ther fngerprnt at crme scenes, wth ther past record collected from them earler [, 2]. Sgnature recognton Each person sgnature s also unque from each other; ths technology s dynamcally used to authentcate a person. he technology s based on pressure and angle used by the person when the sgnature s produced. hs technology s used for e-busness applcatons and other applcatons where sgnature s used for authentcaton [, 4]. Irs recognton Recognton of rs n the eye s called rs recognton. he black dot n the mddle of the eye s the pupl whch s surrounded by a colored part. It does not requre any physcal contact wth any scannng devces and any vdeo acquston system can be used for capturng the rs. Systems based on rs are recognton has substantally decreased n prce and ths s expected to contnue n future. hs technology works well n both verfcaton and dentfcaton modes. Irs recognton has also demonstrated the work wth varous natonaltes of ndvdual []. Face recognton usng LDA Face recognton usng LDA s used to mplement the model for a partcular face and recognze t from a large number of database stored faces wth some real tme varatons as well. LDA (Lnear Dscrmnant Analyss) and related fsher s lnear dscrmnant algorthm whch are used for statstcs and pattern recognton and to fnd the lnear combnaton of features whch separate two or more classes of obects or events. hen LDA technque used for data classfcaton and PCA (Prncpal Component Analyss) technque for feature classfcaton. In PCA, shape and locaton of the data sets change when transformed to dfferent space where as n LDA t doesn t change the locaton of data set but tres to provde class severablty and draws a decson regon. PCA s a dmensonalty reducton technque; LDA whch s used for labeled nformaton of data sets can be proected orthogonal wthout destroyng the nformaton of scatter matrces. In fsher face s combnaton of both LDA and PCA. In ths proecton the data s separated wthout clusterng nto each other and then separated nto scatter matrces dependng upon the varance between class scatter (S b ) and wthn class scatter (S w ). Recognton s wdely under the varaton of front vew, where data sets consst of lmted vew. Lnear dscrmnant analyss he famous example of dmensonalty reducton s PCA technque whch searches for drectons n data that have hghest varance s frequently proect the data. Due to ths some lower dmensonal data are not proected. here are many dffcult ssues such as how many drecton one has to choose s beyond the scope. PCA s unsupervsed technque and does not nclude label nformaton of the data [5]. When two data s and s2 are proected on a plane whch are parallel and very close to each other, such that the varance n the data set wll be n terrble proecton, because all labels get mxed and the useful nformaton wll be destroyed. A useful proecton s orthogonal and wth least overall varance, then the data sets are perfectly separated. In order to utlze the label nformaton n proecton Fsher LDA consders the followng obectve, [6]. 90

3 J (W ) = w w s s B W w w () Where s B s the between classes scatter matrx and s w s the wthn classes scatter matrx. Note that scatter matrces are proportonal to the covarance matrces. MEHODOLOGY he whole recognton process nvolves two: a) Intalzaton process b) Recognton process he ntalzaton process nvolves the followng operatons: a) Acqure the set of face mages called tranng set. b) Calculate the mean adusted mage for all the samples and each class and then the scatter s calculated for between class scatter and wthn class scatter. c) hen the proecton of fsher crteron s satsfed then the face space value s calculated for fsher. he recognton process nvolves the followng operatons: a) Calculate a set of weghts based on the nput mage and then proectng the nput mage n Egen space. b) In ths face of test database s kept and matched wth the nput mage to recognze. Fnger prnt recognton Fngerprnt recognton uses mnutae whch are used for automated verfcaton of matchng between two human fngerprnts. hrough many ntensve research fngerprnts have been dentfed through ther rdges and furrows whch may be called as mnutae. hese rdges are formed are fully developed durng pregnancy and whch wll not change durng the whole lfe tme. A fngerprnt s feature pattern of one fnger and can be compared wth another fnger for dentfcaton and verfcaton [7]. Mnutae are manly classfed nto two types, they are Rdge endng - he abrupt end of a rdge. Rdge bfurcaton - a sngle rdge whch dvdes nto two rdges. Fgure-2. Other mnutae feature n fnger. Fngerprnt based matchng technques may be classfed nto three types: a) Mnutae based matchng: hs s the most frequently used algorthm whch s used for recognton. Fngerprnts are matched by certan algnment of mnutae ponts wth another fnger mnuta and produce the matchng percent of the fngerprnt. hs technology has wdely ncreased varous servces all over the world [8]. b) Correlaton based matchng: wo fngerprnts are supermposed and then correlatons between the pxels are calculated as dsplacement and rotaton angles etc. c) Pattern based matchng: hs method s also known as mage based matchng. It consders the followng whle tryng to recognze whether t s rght loop, left loop, whorl, and arch. It compares the fnger wth the stored template of the database of canddate fngerprnts. Orentaton angles, type and sze of the fngerprnt are matched wth the degree to whch t matches. In ths we are usng mnutae matchng algorthm, whch s now, the backbone of the present technology products. Sgnature recognton In ths, offlne sgnature s taken for verfcaton and recognton system; the sgnatures are composed of specal characters and are not readable most of the tme. It s varyng from one person to another and t should be treated as mage only not as letters and words. In many busness applcatons sgnature s used for authentcaton and authorzaton n legal bank transactons and so the research sgnature verfcaton has ncreased n recent years [9]. In ths, recognton s for dentfyng the sgnature owner. Verfcaton s to dentfy the sgnature s orgnal or forgery. here are two types of forgeres a) Random forgeres b) Smple forgeres Fgure-. wo mnutae features. In Random forgeres the perpetraton knows the sgnature name and sgnature shape. Smple forgeres are produced wthout havng the sgner s sgnature. SRVS (sgnature Recognton and Verfcaton system) s often 9

4 categorzed n two classes: n onlne SRVS the data s obtaned through the electronc tablets where as n offlne sgnature the data s obtaned from the sgnature wrtten on paper. Offlne handwrtng recognton systems are more dffcult than onlne systems as nformaton lke duraton, number of strokes and drecton of wrtng are lost. But, offlne systems have an advantage because they need not requre accessng the processng devces where the sgnature s produced. In ths database of sgnature mages are taken for verfcaton and dentfcaton. It s tested wth the forgery and random sgnature of each person n the database. FACE RECOGNIION INIALIZAION PROCESS Let a face mage be (x,y) wth two dmensonal N by N matrxes of 8-bt ntensty values of 256 gray levels. An mage s consdered as a vector of dmenson N 2, so the sze of mage s 200x80 becomes a dmenson of n dmensonal space, x and y denote par of coordnates n the mage [5]. In order to determne the Egen face of tranng set, 2, 3. n we have to calculate the mean vector. Where M s total number of database mage whch we are takng. hen mean of the total M samples are M n n= ψ = (/ M ) (2) C ) = (/ M AA (5) For ths covarance matrx a set of N 2 N 2 dmensonal matrx s obtaned. Calculatng Egen vector for these dmenson s mpossble due to less compact data pont n mage space (.e., M << N 2), so M- Egen vector are calculated. Consder the Egen vector v of AA. In ths AA can wrte as A A. A Av µ = v (6) Where µ s a scalar are correspondng Egen values of Egen vector v. hen the above equaton s multpled wth A on both sdes of the equaton. M AA Av = Aµ v (7) M M CAv = µ Av (8) M C s the covarance matrx, the rght hand sde equaton Av s called Egen vector of the tranng set of mages, hs Egen vector s multpled wth ndvdual dfference from a mean vector then t s known as Egen face of tranng mages or proected face mage. x Av *( ψ ) (9) k = Fgure-4. Mean mage. he set of devaton from mean vectors {ø ø 2 ø 3.ø n } whch has ndvdual dfference of each tranng mage from a mean vector, where =, 2...M, the tranng set of mages s {, 2, 3. n} φ = ψ (3) In order to calculate the Egen face the prncpal components of the tranng mage should be calculated ths gves set of vectors then the Egen face s obtaned through the Egen vector of the covarance matrx whch s gven by [5]. M C = ( / M ) φ φ n (4) n= n Where n=, 2 M, A = { φ, φ2, φ3... φn} hen the covarance matrx can be wrtten as Let the tranng set of mages face mages be x, x 2, x 3.x m be the M samples wth c classes whch s χ, χ 2. χ c whch denote the number of persons n the database s taken. hen the mean s calculated for proected Egen face mage for each person s =, 2.c [0]. µ = ( / M ) x. (0) k x k χ hen the total average mean calculated for M proected Egen face mage s gven as [6]. M x k k = µ = (/ M ) () he mean are separated from the M samples of the proected Egen face mages the scatter matrx s gven below [5]. S = ( xk µ )( xk µ ) xk χ (2) 92

5 We defne the scatter matrx S and S w then the wthn class scatter can be calculated as follows: S w = S + S 2 (3) hs s formed from above equaton wth no of class s c where =, 2 c, where ths s ntalzaton of wthn class scatter [6]. c S w = S = (4) hen class scatter can be calculated wth class mean and total adusted mean whch s ntalzaton of between class scatter [6]. c S B = χ ( µ µ )( µ µ ) follows = (5) hen the total scatter matrx can be calculated as Where the weghts of proected fsher face mage s Ω, Ωk s the weght of nput mage vector. FINGERPRIN RECOGNIION PREPROCESSING SAGE he steps are mage segmentaton and mage enhancement. In ths mage enhancement the man functon s to mprove the qualty of mage. So the fngerprnt mages obtaned from varous sources are contrasted and clarty must be ncreased. By enhancng the mage the merged rdges and furrows can be clearly extracted. Hstogram equalzaton Hstogram equalzaton s performed for ncreasng the qualty of the mage. hs hstogram equalzaton s used for changng a low contrast mage to a hgh contrast mage. It occupes range from 0 to 255. By ths hstogram equalzaton the deep rdges and valley are dentfed easly n the fnger prnt [8]. p S = S + S (6) w B hen the Egen vector value s calculated for the S total scatter matrx. hen the vector obtaned s called as fsher proected mage. hen the fsher crteron s satsfed by multplyng proected Egen face wth fsher proected mage. In LDA data dmenson are much larger than No of samples N s gven by d>>n. snce S w s a symmetrc and postve sem defnte wth a non sngular n>d. But for S B t s also symmetrc and postve sem defnte ts outer product of two vectors, wth rank one and S B s sngular. hs s fsher crteron functon whch must be satsfed []. w k = s s B W (7) Fgure-5. a) Fngerprnt mage b) hstogram equalzed mage Image bnarzaton he mage bnarzaton s done for convertng 8- bt gray level mage to -bt mage where 0 for rdges and l for furrows or deep valley whch are present n fngerprnt. A locally adaptve bnarzaton method s used to bnarze the mage. Such named method comes from the mechansm of transformng the pxel value to then the value of mean ntensty larger than (6 6) where the pxel belongs to. Where w k s proected fsher face mage. Recognton process he Fsher face mage obtaned s subtracted wth the mage vector obtaned from the Recognton process s an ndvdual mage compared wth the total data base of tranng set. hs vector can be compared usng weghted Eucldean dstance algorthm or weghted hammng dstance algorthm. From that a k th face class s dentfed. hen the face belongs to tranng dataset whch allows to set a threshold otherwse the face wll be unknown. ε = Ω (8) p Ω k Fgure-6. a) Image after bnarzaton b) mage before bnarzaton Image segmentaton Generally Regon of Interest (ROI) s used for recognzng each fngerprnt mage. he rdges and furrows unoccuped mage area s frst dscarded t ust holds background nformaton. hen the remanng effectve boundng area s confusng wthout spurous 93

6 mnutae that are generated out of bound rdges n the sensor. hen the processed fngerprnt mage s dvded nto6x6 non overlappng block sze, followed by gradent calculaton of each block. ROI s done usng two morphologcal operatons called OPEN or CLOSE. he open operaton expands mages and removes peaks produced by background nose. he close operaton s used for shrnkng the mage. hen by subtractng closed regon from open regon the fnal ROI s obtaned by dscardng the bottom, top, rght, left porton [7]. If center pxel s and has only neghbor, then t s rdge endng In ths case both the uppermost pxel wth value and rghtmost pxel wth value have another neghbor outsde the 3x3 wndow due to some left over spkes, so then the two pxels are also matched as branches too, but only one branch s located n small regon Fgure-7. Regon of nterest. Mnutae extracton he processng rdge thnnng s used for removng redundant pxels n the mage. An teratve parallel thnnng algorthm s used for scannng full fngerprnt mage. hs algorthm marks down more redundant pxels and removes the pxels by several scannng. In Matlab Morphologcal operaton the thnnng functon []. bwmorph (bnarymage, thn,nf) hen the thnned rdge map s fltered wth three morphologcal operatons usng H breaks, solated ponts, spkes [8]. bwmorph (bnarymage, hbreak,nf) bwmorph (bnarymage, clean,nf) bwmorph (bnarymage, spur,nf) False mnutae removal Due to nsuffcent amount of nk or over nkng n the fnger prnt mage whch may lead to rdge cross connectons are formed so to keep system consstent ths false mnutae should be removed. he average nter rdge refers to the average dstance between two nter rdge neghbors. Sum up all pxels n the row whose value s one then dvde t by row length where nter rdge dstance D s obtaned [8]. Now 7 false mnutae from m, m2, m3, m4, m5, m6, m7 are removed usng these steps. d(x,y) s the dstance between two mnutae ponts d(bfuracaton,termnaton)<d then 2 mnutae pont n same rdge then remove (case m) d(bfurcaton,bfurcaton)<d then 2 mnutae pont n same rdge then remove(case m2 and m3) d(termnaton,termnaton)=d then ther drectons are concdent then remove(case m4,m5,m6) d(termnaton,termnaton)<d then 2 mnutae pont n same rdge removes (case m7). Fgure-8. hnned mage. Mnutae markng Mnutae markng are done usng 3x3 wndow pxels. If center pxel s wth three values havng neghbors then t s rdge branch [8]. Fgure-0. Mnutae along false mnutae ponts

7 matchng score s generated n the range from 0 to 00 the mnuta ponts n fnger prnt matchng produces rato of percentage score s obtaned [8]. Fgure-. After removng false mnutae. Mnutae match wo mnuta fnger prnt mages are matched through mnuta matchng algorthm to decde whether both fnger prnts are from same fnger or not. If the smlarty of fnger s matched wth larger than threshold then t s matched. It has two stages: algnment stage, match stage [8]. Algnment stage wo fnger prnt mages are matched wth algnment coordnates I and I 2 where ths smlarty set are matched wth each rdge of the reference mnuta ponts. If they are smlar and larger than threshold, then the two sets are transformed to new coordnate systems wth orgn as reference pont and the concdent and reference pont at x-axs. he x-coordnates (x, x2, x3...xn) are ponts on the rdge. A sampled pont on each rdge length L startng from the mnutae pont, the average nter rdge length s L. n value s set to 0 unless the total rdge length s less than 0*L [8]. So the smlarty of correlatng the two rdges are gven below S= m = 0 m = 0 x * X x 2 * X 2 (9) Where (x ~x n ) and (X ~X N ) are the set of mnutae for each fngerprnt mage. If the smlarty score s larger than 0.25 then the fnger s matched else the percentage score s zero. If the two fnger prnt mages are translated and rotated then we get a transformed set of mnuta ponts I and I 2. Match stage In ths I and I 2 are two reference mnuta matched pars must strctly requre these (x, y, θ) parameters. In ths elastc matchng of mnuta s acheved by placng a boundary box around each fnger mnutae ponts. If we are placng rectangle box around the mnuta ponts of the mage t reduces the dscrepancy of mnuta matchng. Matchng wth very small dscrepancy s two fnger prnt mages of an dentcal person. Fnally, a SIGNAURE RECOGNIION PREPROCESSING SAGES In sgnature recognton, both tranng and testng phases are taken and then they are normalzed. he sgnature whch s colored can be scanned to gray. hen the steps performed n post processng are background elmnaton, Nose extracton, wdth normalzaton and thnnng. In the feature extractng stage the global features are extracted and then a post processng stage s performed where the nput s matched wth database of sgnature whch conssts of random and forgery sgnature. Background elmnaton hresholdng s appled for the sgnature to capture sgnature. In ths sgnature occuped n the data area s represented by and back ground area s represented by 0 [9]. Nose reducton In nose reducton, a flter s appled for the bnary mage whch can be used for removng sngle black pxels on the whte background. If the number of black pxels s greater than the number of whte pxels then t chooses black otherwse whte. In wdth normalzaton the sgnature of each person wll have dfference n dmenson. he adusted mage value does not have any effect on heght and wdth rato. hnnng he man am of thnnng s to elmnate thckness dfference of each pen should make dfference durng computaton. So, the pen dfference of sgnature mage nto one pxel thck. hs operaton can done usng morphologcal operaton n matlab. Feature extracton he tranng databases of nputs are taken. Global features provde nformaton about the shape of sgnature and area occuped by the sgnature, center of sgnature s also calculated [9]. Sgnature area he area occuped by the sgnature n number of pxels, whch denotes the densty of the sgnature. Sgnature heght to wdth rato he heght of sgnature s dvded wth wdth of sgnature s also called Aspect Rato of the sgnature. hs aspect rato s approxmately equal for each person. Maxmum horzontal hstogram and maxmum vertcal hstogram he hstogram calculated for horzontal takes a row n that whch row has the hghest value s taken 95

8 horzontal hstogram. he hstogram calculated for vertcal takes a column n that whch column has the hghest value s taken as vertcal hstogram. Horzontal and vertcal center of the sgnature he center of sgnature s calculated for horzontal and vertcal usng ths formula [2]. x max y max x b[ x][ y] center (20) center x= y= x = x max y max x= y= y max x max y y= x= y = x maxy max x= y= b[ x][ y] b[ x][ y] b[ x][ y] (2) After all ths preprocessng steps a rotaton angel of the sgnature s known and boundng box s created for the sgnature to match the sgnature correctly. he same operaton s appled for the nput mage whch s used for verfcaton. In ths database of orgnal sgnature and forgery sgnature are combned. IMPLEMENAION OF FACE RECOGNIION he ranng database obtaned from 5 people s each wth 2 mages wth dfferent facal expressons and testng phase by takng photographs wth dfferent pose of mages whch are not present n the tranng database. he mages are taken low resoluton camera n ths lghtng, background, are the factors whch are consdered. ranng. ranng mages are kept n a partcular folder wth.pg format n whch the program s stored. A total of 60 mages consdered of person wth each 2 mages. 2. hen the tranng mages are the M samples where each matrx s converted nto gray levels and reshaped nto sze of 200x80. hs s a sngle large dmensonal matrx. 3. Where s a matrx of each ndvdual tranng mages. 4. For ths mean s calculated as follows: Z = mean( ) (22) 5. hen the mean s subtracted from the ndvdual mage vectors and kept n matrx A. A = Z (23) 6. Calculatng the Egen face of covarance matrx s C ) = (/ M AA (24) 7. For ths covarance matrx a egen vector s calculated as follows: Eg [ c] = [ V D] (25) Where V s the Egen vector and D s the Egen value. 8. hen the Egen vector when proected Egen space or multpled wth ndvdual dfference from mean vector. hen the Egen face obtaned s as follows X = V A (26) 9. he mean s calculated for total Egen face as follows: P = mean(x ) (27) 0. hen the average mean calculated for each person s also obtaned that s consdered to be R.. A matrx q s obtaned when subtractng total mean P from Egen space X then q matrx s multpled wth transpose of q. then a scatter matrx S s calculated. 2. hen wthn matrx W s summaton of scatter matrx S. 3. he between scatter matrx B s calculated by subtractng a mean R from mean P, and multpled number of classes or number of persons. 4. he total scatter matrx F s the total of wthn scatter and between scatter. 5. Egen vector s calculated for total scatter matrx F s gven as Eg [ F] = [ E U ] (28) E s the Egen vector and U s the Egen value. 6. he Egen vector of the proected fsher mage when multpled wth Egen face of pca the proected fsher face Y s obtaned. Y = EX (29) estng process. he test mages n the Database are reshaped nto 200x80. For real tme applcatons the mage may be taken from web cam also. 2. hen the mage vector of test mages ndvdual dfference s calculated by subtractng mean from mage vector. L = Z (30) 3. A normalzed proected mage J of the nput mage s obtaned. 4. For each column matrx J we calculate the Eucldean norm dfference between the proected fsher face vectors Y. hen by settng a certan threshold value for Eucldean the person wth small value shows the mage s dentfed. Mnutae extracton ncludes mage segmentaton, mage enhancement, fnal extracton stage; mnutae algnment and matchng are done. he steps nvolved n 96

9 mage enhancement are hstogram equalzaton whch s used for ncreasng the contrast of mage. Image bnarzaton s used for convertng gray level mage to btmap mage. In mage segmentaton the steps are rdge flow estmaton and regon of nterest through Matlab s morphologcal operaton. hen the two steps above are known as preprocessng stages. In fnal extracton stage the steps nvolved are rdge thnnng, mnutae markng and false mnutae removal process. In mnutae algnment stage the steps nvolved are fne reference mnutae par, transform mnutae sets. he algnment stage s known as post processng stage. =Sgnature mage he mplementaton of varous pre processng steps n matlab through loopng, the sgnature area, sgnature heght to wdth rato s calculated and global features are extracted the same can be appled to post processng stage and both can be matched for recognton. It shows accepted or reected [9]. hs Egen vector s multpled wth ndvdual dfference from the mean vector where Egen face s obtaned. And ths Egen face can be multpled wth total scatter fsher face then t s fsher crteron. hs shows the followng mage recognzed for gven nput. Case- Case-2 FACE RECOGNIION RESULS Here we are consderng a total of 60 mages wth 5 persons wth each 2 mages, wth varyng facal expressons of each person. he tranng database s shown below Case-3 Fgure-4. Database of face mages. hen the tranng dataset of mages are acqured, mean should be calculated. Fgure-6. Output recognzed from the database. Fgure-5. Mean adusted mage. In ths after normalzaton the Egen face s calculated from the covarance matrx of Egen vector. Fgure-7. Database of mage taken for recognton. 97

10 able-. Accuracy of database mages. No. of mages No of persons accuracy 2 50% % % FINGER PRIN RECOGNIION RESULS he fnger prnt of 5 persons each havng 4 fnger prnts and a total of 20 mages are there ths fngerprnts lot more 00 to 200 mages I have done ust sample for recognton of fngerprnt. he fngerprnt mages are captured by usng a sensor. hen the fngerprnt mages may vary wth qualty from each mage. In ths each fngerprnt template obtaned s matched wth other fngerprnt matchng technque. Fngerprnt verfcaton produces a percentage matchng score of above 0 percent then the fngerprnt mage s some way matched wth each other of the template. he table gven below shows, the hghest percentage scores of each person mage taken from the database. normally taken as mage. he sgnature taken here four of each person whch may have random and smple forgeres ths can be overcome by ths algorthm a total of 20 sgnatures s avalable. In ths cropped mage or boundng box created around the sgnature of both post and pre processng stages are compared for verfcaton or recognton of the sgnature. Fgure-9. Color sgnature mage. Fgure-20. Gray converted sgnature mage. Fgure-2. hnned mage. Fgure-22. Movng sgnature to orgn. Fgure-8. FVC-DB (2004) Database of fngerprnt mages. able-2. Fngerprnt recognton percentage score. Fnger prnt mage Person mage Person2 mage 2 Person3 mage Person4 mage3 Person5 mage Matched wth Personfnger mage 3 Person2 fnger mage 3 Person3 fnger mage 2 Person4 fnger mage Person5 fnger mage 3 Hghest matchng percentage 39.8 Due to nsuffcent nkng or over nkng the matchng percentage score s n the range of 37%. SIGNAURE RECOGNIION RESULS he database sgnatures are taken for recognton whch may not be treated as specal character and t s Fgure-23. Boundary box created mage. Fgure-24. Database of sgnatures whch are taken for recognton. he same steps are also performed n post processng stages than they are matched for verfcaton f t s matched t produces accept else reected. 98

11 Future scope hs paper s based on recognton usng certan bometrc trats. In ths through fuson of all these face, fnger, sgnature algorthms at any three stages can produce better results for recognton of a person and hghly effcent. In face LDA technque has been used whch has more future n neural networks and can produce more accuracy. Wthout fxng a constant dependng upon the condtons the database can be avalable. Fnger prnt recognton s fundamental algorthm for recognton whch has more scope n future by usng varous matchng methods Such as correlaton, pattern matchng. Sgnature can also be done n neural networks whch s treated same as mage whch has hghest matchng ponts s consdered. All these bometrc used are dependent upon the database, and database s dependent upon resoluton of the sensor. he results consdered can be mproved hghly. CONCLUSIONS Face, fnger, sgnature recognton technques avalable can provde better results whereas face recognton through fsher s LDA whch s combnaton of both PCA and LDA uses both Egen face approach and dscrmnant analyss for more practcal results. It s fast, relable and smple. hs works n any constraned envronment. And t depends on head sze and background, lghtng of ths are necessary for ths unambguous technque. In Mnutae matchng algorthm whch varous factors are consderaton at mage sze, qualty of mage, skeletozaton of mage and rectangle box around the mnutae matchng ponts then only t can produce better result. hs matchng of fnger prnt produces a matchng score n range of percentage from 0 to00. In sgnature recognton the sgnature mage s not treated as a specal character. Lot of preprocessng or normalzaton s done for the mage. hen feature extracton can be done through these Global features of the sgnature has been extracted for matchng wth post processed sgnature. he sgnature depends on heghtwdth rato, area of sgnature, background elmnaton of the mage. REFERENCES [] A.M. patl, Satsh R. Kohle and Pradeep M. Patl. Face Recognton by pca technque. 2 nd nternatonal conference on emergng trends n engneerng and technology, ICEE-09. [4] H. Hassanpour and H. Yousefan. 20. An Improved Pxon- Based approach for Image Segmentaton. ransactons A: Bascs, January. 24(): [5] Anl K. Jan, Arun Ross and Sall prabhakar An ntroducton to bometrc recognton. IEEE ransactons on crcuts and systems for vdeo technology. 4(), January, DOI [6] Lsh zhang, Dehong wang and Shengzho gao. Applcaton of mproved fsher lnear dscrmnant analyss approach. Appeared at [7] H B Kekre and V A Bhadr. Fnger prnt core pont detecton algorthm usng orentaton feld based multple features. Internatonal Journal of Computer Applcatons, ISSN , (5). [8] Sangram Bana and Davnder Kaur. Fnger prnt recognton usng mage segmentaton. Internatonal Journal of Advanced Engneerng Scences and echnologes. 5(): [9] Emre ozgunduz tuln senturk and M. Elf karslgal. Off lne sgnature verfcaton and recognton by support vector machne. Yldez techncal unversty, yldz, Istanbul. 3 th European Sgnal Processng Conference Antalya. [0] homas Heseltne, Nck pears and Jm Austn Combnng multple face recognton systems usng fsher s lnear dscrmnant. he Proceedngs of the SPIE Defense and Securty Symposum. DOI: 0.7/ [] Andrew Ackerman Fnger prnt recognton. Professor Rafal Ostrovsky. Do: / molecules [2] Maya V. Kark, K. Indra and S. Sethu Selv Off-lne sgnature recognton and verfcaton use neural networks. Internatonal conference on computatonal ntellgence and multmeda applcatons. Do 0.09/ICCIMA [2] Pryadarsn M.J.P., K. Murugesan, S.R. Inbathn and V. Kumar Effcent face recognton n vdeo by bt planes slcng. J. Comput. Sc. 8: DOI:0.3844/cssp [3] H. Hassanpour and S. Asad. 20. Image qualty Enhancement usng pxel wse Gamma. ransactons B: Applcaton. 24-(4):