A Robust Hierarchical approach to Fingerprint matching based on Global and Local Structures

Transcription

1 nternatonal Journal of Appled Engneerng Research N Volume 13, Number 7 (2018) pp Research nda Publcatons. A Robust Herarchcal approach to Fngerprnt matchng based on Global and Local tructures 1 hoba Lesle and 2 C.P. umath 1 Assocate Professor, Department Of Computer cence, Women s Chrstan College, Chenna, nda. 2 Assocate Professor, Department Of Computer cence, DNB Vashnav College for Women, Chenna, nda. Abstract Among the varous bometrc technques for authentcaton, fngerprnt recognton for personal verfcaton and dentfcaton has receved consderable attenton over the past decades because of the dstnctveness and persstence propertes of fngerprnts. Automatc matchng phase s one of the man stages n fngerprnt recognton. n ths paper, a novel two-step herarchcal approach to fngerprnt matchng s proposed based on global Drectonal Varance Features (DVF) and local cues. At the frst level, the matchng reduces to fndng the Eucldean dstance between the DVFs of the nput and stored fngerprnts extracted from global characterstcs and then uses the presented local structure representaton based on spatal relatonshps between the mnutae and reference pont for accurate matchng at the next level. Fnal matchng or smlarty score s evaluated by combnng DVF and Local tructure (L) matchng scores of the query and template fngerprnts. The proposed matchng system has been tested on FVC2002 databases for ts accuracy and fdelty on varous qualtes of fngerprnts and the results show a sgnfcant reducton n the Equal Error Rate (EER) and processng tme when compared to other methods valdated on the same database. Keywords: Eucldean dstance, Reference pont, Drectonal varance, Rdge count, Mnutae. NTRODUCTON Bometrcal dentty authentcaton systems based on fngerprnt analyss have been deployed n a wde range of applcaton domans rangng from law enforcement and forenscs to unlockng moble phones because of ts easy accessblty, unqueness and relablty. Dependng on the context of the applcaton, the fngerprnt authentc system may be ether a verfcaton system or an dentfcaton system. A verfcaton system authentcates a person s dentty by comparng the captured fngerprnt wth her/hs prevously enrolled fngerprnt reference template [1]. An dentfcaton system recognzes an ndvdual by searchng the entre enrolment template database for a match [1]. The fngerprnt feature extracton and matchng algorthms are usually qute smlar for both fngerprnt verfcaton and dentfcaton problems [1]. Though a large number of fngerprnt matchng algorthms have been proposed n the lterature, matchng partal and nosy fngerprnts contnues to be an mportant challenge. When a partal fngerprnt does not nclude one or more sngular ponts, common matchng approaches based on algnment of these structures fal. The other challenges are due to large ntra-class varatons n fngerprnt mages of the same fnger and nter-class smlarty between fngerprnt mages from dfferent fngers. The exstng matchng approaches can be classfed nto mage-based, mnutae based and non-mnutae feature based such as rdge shape, texture nformaton etc. The mage-based methods compute the correlaton of the two fngerprnt mages to determne the smlarty between them ether n the spatal or frequency doman [3]-[5]. The matchng algorthm proposed n [3] calculated 12 dfferent cross correlatons based on certan radus values from the reference pont for both the nput and stored mages to determne whether the mages correspond to the same fngerprnt. Local correlaton of regons around the mnutae was utlzed to estmate the degree of smlarty between two fngerprnt mages as n [4]. However, these methods are not robust to nose and non-lnear dstortons and requres the whole texture or mage for matchng. Mnutae-based matchng conssts of fndng the algnment between the template and the nput mnutae feature sets that result n the maxmum number of smlar mnutae pars [7-13]. A graph based fngerprnt hashng algorthm was proposed n [8] for recognton usng core and mnutae ponts. Planar graph and Delaunay trangulaton algorthms were used for generatng characterstc vector n [11] assocated wth each mnutae and a smlarty coeffcent was computed by comparng the characterstc vector. The proposed mnutae representaton n [13] ncorporated orentaton nformaton around the mnutae and Greedy algorthm was used to construct the set of smlar mnutae. Though mnutae based technques are popular and wdely used, fngerprnt matchng based exclusvely on mnutae may not be sutable for matchng low-qualty fngerprnts thereby reduces the matchng accuracy. Non-mnutae feature based matchng compares fngerprnts usng Level 3 features (pores and rdge contours) [2], [14], [15]. Topologcal nformaton on rdge patterns s utlzed n rdge-based matchng. A matchng technque based on rdge features detected usng Hough transform was presented n [14] and the matchng score was based on the number of matched rdges n the nput and template fngerprnt. Matchng schemes ncorporatng Level-3 features was proposed n [19][20] n conuncton wth mnutae features to acheve hgher matchng accuracy but detecton of pores s possble only n hgh resoluton fngerprnt mages of over 1000 dp. The computatonal complexty of many of these exstng matchng methods s hgh. 4730

2 nternatonal Journal of Appled Engneerng Research N Volume 13, Number 7 (2018) pp Research nda Publcatons. n ths paper, a fngerprnt matchng scheme operatng n two stages has been proposed that s based on representaton schemes (DVFs) that capture the global pattern of rdges and valleys and a novel local mnutae representaton. Drectonal varance Features (DVFs) are compact fxed length codes constructed usng fngerprnt orentaton mage and a unque reference pont. A herarchcal approach s adopted n the matchng process. ntally, the matchng reduces to fndng the Eucldean dstance between the DVFs of template and nput fngerprnts extracted from global characterstcs and then uses the local structure representaton for fner matchng. Local representatons are constructed for those mnutae ponts that are at a fxed dstance from the reference pont for accurate matchng at the second level. No pror complex global algnment s requred for the proposed matchng scheme, as the unque reference pont detected n the nput and stored fngerprnt takes care of translaton and the local mnutae representatons are characterzed by attrbutes that are nvarant to translaton and rotaton. The matchng score at the second level combnes the smlarty score of DVFs matchng and mnutae matchng. The overvew of the proposed fngerprnt matchng system s shown n Fgure 1, and the each stage s explaned n the followng sectons. The rest of the paper s organzed as follows: ecton 2 presents the fngerprnt preprocessng approach used n ths work. ecton 3 explans the global feature extracton (DVF) methodology. n secton 4, the generaton of suggested local features based on mnutae s descrbed. ecton 5 presents the proposed herarchcal fngerprnt matchng method. Expermental dscusson and results are provded n secton 6 and fnally secton 7 concludes the paper. Fgure 1. Block Dagram of the proposed fngerprnt matchng scheme 4731

3 nternatonal Journal of Appled Engneerng Research N Volume 13, Number 7 (2018) pp Research nda Publcatons. FNGERPRNT PRE-PROCENG Fngerprnt segmentaton s an essental process to solate the foreground area that conssts of an area of nterest (.e. regons of rdges and valleys of the fngerprnt mpresson) from the background to avod extracton of features from the background regon. As foreground regons n the fngerprnt mage exhbt hgh gray-scale varance and the background exhbts very low varance, a method that s based on gray ntensty varance level s used to segment the foreground from the background. The fngerprnt mage s then enhanced usng Gaussan band pass flter n the frequency doman [21]. Ths s done to reduce nose and mprove the legblty of the fngerprnt. The process of segmentaton and enhancement s carred out as descrbed n [21]. except for partal fngerprnts. Ths reference pont localzaton approach s based on the herarchcal analyss of orentaton coherences on varyng neghbourhood [24]. The orentaton consstency s low n hgh curvature and nosy areas than n smooth areas [24]. Mult-scale analyss of the orentaton consstency s done to search the block of local mnmum consstency from the largest scale to the fnest scale as shown n Fgure 2. Each scale s based on the outsde surroundng 8*s blocks of (2s + 1) x (2s + 1) neghbourhood of the centre block and s denotes the correspondng scale of the mult-scale analyss [24]. n ths work, four scales are used. Drecton of curvature technque s used to determne the unque reference pont. EXTRACTNG GLOBAL CHARACTERTC (DVF) t s desrable to obtan fngerprnt representatons that are scale, translaton and rotaton nvarant. cale nvarance s not a sgnfcant problem snce most of the fngerprnt mages could be scaled as per the dp specfcaton of the sensors [2]. The man stages n the global feature extracton scheme are descrbed below: Orentaton Feld Estmaton As fngerprnt Orentaton Feld (OF) descrbes the drectonalty of local rdge structure of a fngerprnt, ts estmaton provdes rch nformaton for dentfyng global characterstcs. Hence, ths stage s amed at obtanng a relable Orentaton mage (O) (.e. a two-dmensonal matrx) whose elements encode the local orentaton of the fngerprnt rdges. The domnant orentaton of a nonoverlappng mage block of sze wx w s estmated by the least mean square method based on the gradents [22]. The components of the gradent g and g are determned usng x the classcal obel convoluton mask. The angle wth maxmum rate of change n gray ntensty determnes the drectonal feld for that block. As orentaton feld mage thus computed from nosy fngerprnts wth poor rdge structure may contan several unrelable elements, an orentaton regularzaton algorthm that s based on adaptvely changes the smoothng neghbourhood sze after analyzng the orentaton consstency s used n ths work as presented n [23]. A low-pass Gaussan kernel of adaptve sze s convolved wth the adaptve neghbourhood sze block durng the smoothng process. After regularzaton, the OF s more stable and resstant to nose for further processng. y Fgure 2. Mult-scale analyss of orentaton consstency Ths technque consstently localzes reference pont accurately for all classes of fngerprnts as analysed n [25]. Global Fngerprnt representaton To facltate fngerprnt matchng at the global level, a compact fngerprnt representaton, Drectonal Varance Features (DVFs) usng the orentaton mage and a unque reference pont n the fngerprnt mage as proposed n [29] s used. The orentaton dfference between the 16 radal drectons wth /8 nterval as depcted n Fgure 3 from the reference pont and the local rdge orentatons along the correspondng radal are analyzed and approxmated by usng the absolute sne component [24]. Reference pont localzaton usng fngerprnt O A unque reference pont for subsequent feature extracton stage s detected from the orentaton feld mage regularzed usng adaptve neghbourhood analyss. Ths pont s taken as the pont wth maxmum curvature on the convex rdge whch s usually located at the central regon of the fngerprnt Fgure Radal drectons (0-15)[n red] wth π/8 nterval from the reference pont [n blue] 4732

4 nternatonal Journal of Appled Engneerng Research N Volume 13, Number 7 (2018) pp Research nda Publcatons. The domnant two radal drectons wth two least drectonal varances ncluded n the DVFs for the classfcaton process n [29] has not been consdered for the matchng process as they are necessary only to establsh the dfferent predefned fngerprnt classes. Hence, only the orentaton pattern determned by drectonal varances computed from the reference pont usng the orentaton feld mage along 16 radal drectons (0º, 22.5º, 45º, 67.5º, 90º, 112.5º, 135º, 157.5º, 180º, 202.5º, 225º, 247.5º, 270º, 292.5º, 315º, 337.5º) consttute the feature vector to establsh a match/non-match at level 1. LOCAL TRUCTURE (L) REPREENTATON Local structures around the unque reference pont detected n fngerprnt mages are used for accurate matchng at the second level. The methodology proposed n ths paper for local feature extracton s based on mnutae ponts (rdge endngs and bfurcatons) and rdges. The feature extracton stages are descrbed below: Gray cale Rdge keletonzaton The common mnutae ponts, bfurcaton and rdge end ponts has propertes to unquely descrbe a fngerprnt mage. A compact representaton to remove redundant nformaton as much as possble s useful to extract mnutae ponts. Hence, skeletonzaton of rdges n fngerprnts becomes an essental step n the process of mnutae extracton. Extracton of relable mnutae heavly reles on the qualty of the rdge skeleton. To speed up the thnnng process and to obtan relable mnutae from fngerprnt mages, drect gray scale skeletonzaton of fngerprnt rdges usng parallel algorthm as proposed n [26] has been n ths work. Ths methodology does not go through the process of bnarzaton unlke many other thnnng approaches. Fgure 4 (a) Orgnal FP mage (b) Mnutae ponts rdge endngs [n red] and bfurcaton ponts [n green] extracted from the skeleton mage Feature Vector Constructon After obtanng the skeletonsed rdges and mnutae nformaton from the fngerprnt mages, features around the unque reference pont s used to create the local feature vector. The proposed local feature extracton process for matchng do not take nto consderaton the poston of the mnutae unlke most of the conventonal mnutae based matchng [11][20][30] as t becomes rrelevant when dealng wth large perturbatons due to rotaton. Moreover, localzng a mnutae pont as reference n both the nput and template may also not be accurate. The components of the suggested local feature vector constructed for subsequent fngerprnt matchng process at the fner level explots the fact that the Eucldean dstance and spatal relatonshp between the reference pont and a mnutae pont remans constant rrespectve of the fngerprnt orentaton as shown n Fgure 5. Mnutae extracton Process The wdely used concept of Crossng Number (CN) defned by Rutovtz s used for extractng mnutae ponts n ths work and s gven below: 8 1 wth P9 P1 1 CN( P) P P (1) For a non-zero centre pxel P, CN P counts the number of background to non-zero transtons. Usng Eq.1 fcn P 1, then t s corresponds to a rdge pont and fcn P 3, t corresponds to rdge bfurcaton. The extracted bfurcaton ponts and end-ponts n thnned fngerprnt mage are shown n Fgure 4. Fgure 5. nterconnectng lnes between reference pont and mnutae ponts at dfferent orentatons f we extract local features for all the mnutae n the fngerprnt mage, the sze of the template wll be very large and thereby decreases the system effcency. Hence, only the mnutae ( m ) that are closer to the reference pont ( R ) are selected as only mnutae ponts n the nput and enrolled fngerprnts are enough to be matched [31]. The selecton rule s based on dstance functon D appled on M mnutae ponts detected n the fngerprnt mage and s gven by m R D, where {1,2,... M} (2) 4733

5 nternatonal Journal of Appled Engneerng Research N Volume 13, Number 7 (2018) pp Research nda Publcatons. The value of D s a constant that s approprately determned expermentally. The local characterstcs recorded for each canddate mnutae m that satsfes Eq.(2) consdered for fngerprnt matchng s gven below : Fgure 6. Local characterstcs consdered for each canddate mnutae ) Mnutae type ( T ) : The two classcal types of the mnutae ponts are consdered (.e. rdge endngs or rdge bfurcaton) ) Dstance or length (D): The length of the edge connectng the mnuta m from the reference pont as ) shown n Fgure 6. The Eucldean dstance between the th mnuta pont, reference pont R( p, q ) s obtaned from: 2 2 D m ( a b ) and the D ( a p) ( b q) (3) Rdge count (C ): The number of rdges ntersectng the lne segment connectng the mnuta pont (m ) and the reference pont (R). t s determned by the number of 0 to non-zero transtons along the segment mr of the skeleton mage. The local structure (L) wth respect to mnuta(m ) s defned as L( m ) T, D, C where 1,2,... N and N s the total number of canddate mnutae. FNGERPRNT MATCHNG The proposed herarchcal fngerprnt matchng algorthm works n two levels. The Eucldean dstance between the DVFs of template and query fngerprnts extracted from global characterstcs as descrbed n secton 3 s utlzed for the matchng process at the ntal level. Based on the decson taken n the frst step, matchng proceeds to the next level usng the local structure representaton as dscussed n secton 4 for fner matchng. For each fnger, sx templates are stored correspondng to sx mpressons of varous mage qualtes that nclude partal and nclned fngerprnts. The DVFs correspondng to each template based on drectonal varances along 16 radal drectons [0,22.5,45, ] wth /8 nterval s denoted as[ v 1, v 2, v 3, v 4... v 16], where 1,2,..6 correspondng to sx enrolled templates. Let [ q1, q2, q3, q4... q 16] denote the DVF of the nput/query fngerprnt. The Eucldean dstances d (, ) are computed between the DVFs of the nput fngerprnt and the sx stored templates. The mnmum of the sx scores (mn_ s ) corresponds to the best algnment of the fngerprnts beng matched denoted by: ms (, ) mn_ s(( d(, )) (4) DVF Based on the global matchng score a coarse flterng of fngerprnts s made to drectly determne the exstence of a match and non-match usng two threshold values t 1 and t 2 respectvely. A score below t 1 wth mnmum FAR s regarded as a match and a score above t 2 s regarded as a nonmatch. f the computed matchng score s n the range of t 1 and t 2, fngerprnt matchng based on local fngerprnt representatons (Ls) s performed n the next level for fner matchng. The match score may le between t 1 and t 2 under the followng possble scenaros: The nput template and the stored template may be from the same fnger, but the stored features are not nvarant to large perturbatons. The nput and stored fngerprnts are from dfferent fnger, but they are of the same class exhbtng smlar drectonal pattern around the reference pont. Only those cases as mentoned above passes on to the next stage for fner matchng and thus the overall computatonal complexty of the matchng has been greatly reduced to a great extent. Though global characterstcs may be dscrete, t s not suffcent to dentfy fngerprnts that are globally smlar but wth dfferent local detals, such as from twns fngers. Hence, these fngerprnts need a combnaton of global and local structures for accurate matchng. The second level matchng s based on local features constructed for each mnuta that are at a fxed dstance from the reference pont as descrbed n secton 4.3. Let L( m ) and L( m ) mnuta, denote the local structure representaton of a m n stored fngerprnt mage and m n nput mage respectvely, 1,2,... N and 1,2,... N. N and N are the total number of canddate mnutae n and respectvely. For each L( m ) n, a matched dstance ( D n and D n ) s taken as the reference between two Ls n and. As dstortons are nevtable when mappng a three- 4734

6 nternatonal Journal of Appled Engneerng Research N Volume 13, Number 7 (2018) pp Research nda Publcatons. dmensonal fngerprnt onto a two-dmensonal plane and also due to varatons n skn condtons, a small dstance error tolerance ( t) s consdered between D n and D n (.e. D D t ). f an exact match could be establshed between correspondng T andt, and between C and C as well, then the two Ls are consdered to be a match. mlarty between two coeffcent vectors representng the two fngerprnts and s calculated accordng to Eq. 5. M 2xk (, ) ( N N ) / 2 L where k s the number of matched mnutae n and. f suffcent number of correspondences could be made between mnutae of and rrespectve of ther orentatons as shown n Fgure 7, then the two fngerprnt mages are consdered to be from the same fnger. (5) tep 3: f the value of mn_ s s less than a threshold t 1, a correspondence between the fngerprnts prevals, go to step 8, else f mn_ s s greater than a threshold t 2, a match does not exst, go to step 8. tep 4: Gven a set of local feature vectors Ls extracted from two fngerprnts, ( T, D, C ) and ( T, D, C ) correspondng to mnutae m and m respectvely, they are consdered smlar f they satsfy the followng crtera ( D D t ) ( T T ) ( C C ) tep 5: Repeat step (4) and determne the maxmum number of matched Ls correspondng to canddate mnutae n nput and stored fngerprnt mages. tep 6: Evaluate the matchng score ( M L ) by applyng Equaton 7 usng local structure features. tep 7: Fnally, the matchng scores obtaned usng DVFs and Ls are combned usng Equaton 8 to decde whether there exsts a smlarty between the nput and stored fngerprnt templates. tep 8: End Fgure 7. Local matchng n the fngerprnt mages: proecton of stored L on the nput doman A decson usng Eq.5 for a match/non-match cannot be made exclusvely based on the local representatons usng mnutae, as detecton of mnutae may not be accurate for low-qualty fngerprnts. Hence the smlarty scores between and obtaned from DVF matchng and L matchng have been combned to derve a fnal matchng score that s represented as follows: 1 CM(, ) x ML (, ) ms (, ) (6) DVF Hgher the value of CM (, ), greater s ther smlarty. The processng steps of the proposed fngerprnt herarchcal matchng algorthm are as follows: Algorthm: [Herarchcal Fngerprnt Matchng] nput: DVFs and Ls of and Output: Match/Non-Match tep 1: Compute the Eucldean dstances between the DVFs of the nput and the stored templates. tep 2: Preserve the mnmum of the scores mn_ s that correspond to the best algnment of the nput and the enrolled fngerprnt. REULT AND DCUON n ths secton, the recognton performance of the proposed fngerprnt matchng algorthm s expermentally analysed and compared wth other exstng approaches. Experments have been conducted on FVC2002, Db1 and Db2 fngerprnt databases, the reason beng that t was acqured wth a optcal sensor of medum-hgh qualty, makng t sutable for evaluatng algorthms to be used n cvl and moble applcatons. The sze of the fngerprnt mages n Db1 and Db2 are 388 x 374 and 296 x 560 pxels respectvely wth 500 dp and 256 gray levels. The mages n the database are of varyng qualtes wth creases, scars and smudges and also nclude partal and nclned fngerprnts as well. Each database has 880 fngerprnts (8 mpressons from the same fnger). The proposed fngerprnt matchng algorthm presented n ths paper has been mplemented n MATLAB. No reecton mechansm s used to dscard fngerprnt mages n the database to evaluate the performance of the proposed matchng algorthm. As a two step herarchcal approach s adopted for fngerprnt matchng n the proposed algorthm as dscussed n secton 5, expermental analyss have been conducted at each level. At the frst level, to establsh the verfcaton accuracy of the proposed fngerprnt representaton based on global characterstcs and the matchng approach, each mpresson s compared wth other mpressons of the same fnger for genune matches and for mposter matches, each mpresson s matched wth all the mpressons of dfferent fngers. Hence, 4735

7 nternatonal Journal of Appled Engneerng Research N Volume 13, Number 7 (2018) pp Research nda Publcatons. for each database, 3080 genune matchers are executed and 348,800 mposter matches are executed. The matchng score n the frst level s computed based on the Eucldean dstance between two DVFs constructed as descrbed n secton 3.3. None of the genune matchng score s zero as mages from the same fnger had not generated dentcal DVFs because of dstortons and rotaton. The decson s based on a boundary (.e. threshold) that mnmzes FAR and FRR. FAR and FRR values for dfferent decson thresholds as a result of matches performed n ths work after the ntal matchng step usng global characterstcs are shown n Table 1. When FAR s 2.3%, FRR s 2.25% and Equal Error Rate (EER) s 1.82%. The effcency of the proposed fngerprnt matchng based on DVFs has been compared wth the Fngercode matchng scheme as proposed n [2], and Fgure 8 shows the Recever Operatng Characterstc (ROC) curves for the two matchers evaluated on FVC2002 databases. Table 1: False Acceptance and False Reecton Rates wth dfferent threshold values for the FVC2002 DB1 and DB2 databases Threshold value FAR (%) FRR (%) Fngercodes s less than a threshold, a decson s made that the two mages come from the same fnger, otherwse a decson that the two mages come from dfferent fngers s made. Determnng the threshold s a tradeoff between the two types of errors (FAR and FRR). f a hgher threshold s chosen, the genune acceptance rate s lower, but FAR may be hgher and vce versa [2]. n ths proposed technque, at the frst level matchng, two thresholds t 1 and t 2 are chosen to determne match and nonmatch respectvely. A decson s made that the two mages come from the same fnger f the match score ms based DVF on the Eucldean dstance of the DVFs of and s less than t and from dfferent fngers f greater than t 2. Threshold t 1 s 1 chosen wth mnmum FAR and threshold t2 s chosen wth sudden rse n the mposter matches. The values of t1 and t2 chosen are 0.1 and 0.6 respectvely n ths work. There s an uncertanty n the fngerprnt mages whose matchng scores les between t 1 and t 2 as to whether they are from the same fnger or not. Ths s due to the fact that the stored fngerprnt representaton may not be nvarant to large perturbatons or both the nput and stored templates may be from dfferent fngers but belongng to the same class exhbtng smlar pattern around the reference pont as the DVFs used for matchng captures more of the global pattern. t has been observed that genune matches are hgh and mposter matches are low even for low-qualty mages and hence robust to nose. amples of possble fngerprnts whose smlarty scores ( msdvf ) lyng between t 1 and t 2 are shown n Fgure 9 and these progresses to the second level for accurate matchng process. Expermental analyss on FVC2002 database for the proposed herarchcal strategy shows that, on the average 78.8 % of the fngerprnts get fltered after the frst level matchng wth ether a match or non-match decson. Moreover, the feature vector (DVFs) s very compact and requres only 16 bytes rrespectve of the mage sze. The fner second level matchng s performed based on the local structures Ls extracted from and as dscussed n secton 5. The small spatal dstance threshold value t (1.5 n ths work) s chosen after analyzng the Ls generated for varous fngerprnt mages. Fgure 8. ROC curves of the proposed matchng usng DVFs and Fngercode [2] The bass for choosng to compare the proposed method wth [2] s that t has been the most popular technque to match fngerprnts based on texture nformaton durng the past decade. n [2], f the Eucldean dstance between two Fgure 9 (a) Fnger mpressons belongng to the same fnger (b) mpressons of dfferent fngers belongng to the same class exhbtng smlar patterns around the reference pont. 4736

8 nternatonal Journal of Appled Engneerng Research N Volume 13, Number 7 (2018) pp Research nda Publcatons. t has been observed that the combned matchng score (CM(,)) computed usng Equaton 8 based on global attrbutes and local structures s a) Between 1 and 2 for fngerprnt mages belongng to the same class but dfferent fnger. b) Less than 1 for mages belongng to dfferent class and fnger. c) Between 2 and 3.5 for fngerprnt mages belongng to the same fnger but wth dfferent orentatons wth large perturbatons. d) Greater than 4 for same fngerprnt wth smlar orentaton. Based on these observatons the match/non-match threshold (T=3) has been chosen. f the smlarty score s greater than T, then the decson that the two mages belong to the same fnger s made, or else a decson that the two mages orgnate from dfferent fngers" s made. Table 2 presents the EER results as recorded n the respectve papers for FVC2002 databases along wth the proposed matchng scheme and ther correspondng ROC curves s shown n Fgure 10. The processng tme of the proposed system s gven n Table 3. Table 2: Matchng performance (EER%) of exstng algorthms wth proposed method Algorthm Feature/tructure Topology EER(%) Flterbank[2] Fngercode 4.87 K-plet[28] Mnutae-graph 1.5 Xang[9] Mnutae Tensor matrx(mtm) 0.8 Rodrgues [11] Mnutae-Planar Graphcs 1.14 Proposed(tage 1) DVF 1.6 Proposed(tage 2) DVF +L(mnutae) 0.65 Table 3: Processng tme of the proposed method DVF Feature Extracton Matchng (1:1) Feature Extracton L Matchng (1:1) Fgure 10. Comparson of the proposed method along wth exstng methods usng ROC curves for FVC2002 database CONCLUON The proposed herarchcal matchng algorthms have used features based on global characterstcs (DVF) that exhbt hgh dstnctveness among dfferent classes and local structure matchng (L) that can tolerate hgh rotaton dstortons. Expermental results show that the overall accuracy of the proposed matchng system by combnng results of ndependent matchers based on DVF and L fngerprnt representatons has sgnfcantly mproved (EER 0.654%). The developed matchng system can be deployed to control physcal devce access by ntegratng fngerprnt recognton nto laptops, tablets, smart phones and other electronc devces that have personal and senstve data. REFERENCE [1] Davde Malton, Daro Mao, Anl K.Jan, all Prabhakar, Handbook of Fngerprnt Recognton, 2nd Edton. New York: prnger-verlag, [2] Anl K. Jan, Fellow, all Prabhakar, Ln Hong, and harath Pankant, Flterbank-Based Fngerprnt Matchng, EEE Transactons on mage Processng, Vol. 9, No. 5, [3] Abdullah Cavusoglu and alh Gorgunoglu, (2007), A Robust Correlaton Based Fngerprnt Matchng Algorthm for Verfcaton, Journal of Appled cences 7, pp [4] Karthk Nandakumar and Anl K. Jan,(2004), Local Correlaton-based Fngerprnt Matchng, Conference proceedng of Computer Vson, Graphcs and mage Processng, Kolkata, nda, pp [5] Hayun Xu, Raymond N. J. Veldhus, Asker M. Bazen, Tom A. M. Kevenaar, Ton A. H. M. Akkermans and Berk Gokberk,(2009), Fngerprnt Verfcaton Usng pectral Mnutae 4737

9 nternatonal Journal of Appled Engneerng Research N Volume 13, Number 7 (2018) pp Research nda Publcatons. Representatons,EEE Transactons On nformaton Forenscs And ecurty, Vol. 4, No. 3, pp [6] hoba Dyre and C.P. umath, A urvey on Varous Approaches to Fngerprnt Matchng For Personal Verfcaton And dentfcaton", nternatonal Journal of Computer cence and Engneerng urvey (JCE), Vol.7, No.4, pp. 1-7, [7] wasokum Gabrel Babatunde, Fngerprnt Matchng usng Mnutae-ngular ponts Network, nernatonal Journal of gnal Processng, mage Processng and pattern Recognton, Vol.8, No.2, pp , 2015 [8] Pryanka Das, Kannan Karthk, and Boul Chandra Gara, (2012), "A robust algnment-free fngerprnt hashng algorthm based on mnmum dstance graphs", Pattern Recognton 45, no.9, pp [9] Xang Fu and Juffu Feng (2015), Mnuta Tensor Matrx: A New trategy for Fngerprnt Matchng, PLo ONE 10(3): e do: /ournal.pone [10] Pablo Davd Gutérrez, Mguel Lastra, Francsco Herrera, and José Manuel Benítez,(2014), A Hgh Performance Fngerprnt Matchng ystem for Large Databases Based on GPU, EEE Transactons On nformaton Forenscs And ecurty, Vol. 9, No. 1, January [11] R.M. Rodrgues, C.F.F. Costa Flho, M.G.F. Costa,(2015), Fngerprnt verfcaton usng characterstc vectors based on planar graphcs, gnal, mage and Vdeo processng, prnger, Vol.9. ssue 5, pp [12] Jn Q and Yangsheng Wang,(2005), A robust fngerprnt matchng method, Pattern recognton, Vol 38, ssue 10, pp [13] Marus Tco and Paul Kuosmanen,(2003), Fngerprnt matchng usng an orentaton-based mnuta descrptor, EEE Transactons On Pattern Analyss And Machne ntellgence, Vol. 25, No. 8 pp [14] Aparecdo Nlceu Marana and Anl K. Jan, (2005), Rdge-Based Fngerprnt Matchng Usng Hough Transform, EEE Computer Graphcs and mage Processng, 18th Brazlan ymposum pp [15] Lors Nann and Alessandra Lumn,(2008), Local bnary patterns for a hybrd fngerprnt matcher, Pattern Recognton 41,no.11, Pg [16] Raffaele Cappell, Matteo Ferrara, A fngerprnt retreval system based on level-1 and level-2 features, Expert ystems wth Applcatons, Volume 39, ssue 12, 2012, Pages [17] Danel Peralta, Mkel Galar, saac Trguero, Danel Paternan, alvador García, Edurne Barrenechea, José M. Benítez, Humberto Bustnce and Francsco Herrera, A survey on fngerprnt mnutae-based local matchng for verfcaton and dentfcaton: Taxonomy and expermental evaluaton, nformaton cences 315 (2015) [18] Wonune Lee, ungchul Cho, Heeseung Cho, Jahe Km, Partal fngerprnt matchng usng mnutae and rdge shape features for small fngerprnt scanners, Expert ystems Wth Applcatons 87 (2017) [19] Anl Jan, Y Chen, and Meltem Demrkus, (2007), Pores and Rdges: Hgh-Resoluton Fngerprnt Matchng Usng Level 3 Features, EEE Transactons On Pattern Analyss And Machne ntellgence, Vol. 29, No.1, pp [20] Anl K. Jan and Janang Feng, (2011), Latent Fngerprnt Matchng, EEE Transactons On Pattern Analyss And Machne ntellgence, Vol. 33, No. 1, pp [21] hoba Dyre, and C. P. umath, Hybrd approach to enhancng fngerprnt mages usng flters n the frequency doman,. n: EEE nternatonal Conference on Computatonal ntellgence and Computng Research (CCC), 1-6 (2014). [22] Asker M. Bazen and abh H. Gerez, ``Drectonal Feld Computaton for Fngerprnts Based on the Prncpal Component Analyss of Local Gradents, n Proceedngs of ProRC2000, pp , [23] hoba Dyre and C.P umath, Relable Orentaton Feld Estmaton Of Fngerprnt Based On Adaptve Neghborhood Analyss, CTACT Journal on mage and Vdeo Processng 07 (2017) [24] Manhua Lu, Xudong Jang, Alex Chchung Kot, Fngerprnt Reference-Pont Detecton, EURAP Journal on Appled gnal Processng, No.4, pp , [25] hoba Dyre and C.P.umath, Performance Analyss of Fngerprnt Reference Pont Detecton Technques based on Regularzed Orentaton Model", nternatonal Journal of Computer cence and nformaton ecurty, Vol.15, No. 4, pp , Aprl [26] Dyre., umath C.P., Enhanced Gray cale keletonzaton of Fngerprnt Rdges Usng Parallel Algorthm, n: Recent Trends n mage Processng and Pattern Recognton RTP2R 2016, Communcatons n Computer and nformaton cence, vol 709. pp , prnger, ngapore, 2017 [27] Guo, Z., Hall, R.: Parallel thnnng wth two sub teraton algorthms. Commun.ACM 32, (1989) [28] harat Chkkerur, Venu Govndarau and Alexander N. Cartwrght, K-plet and Coupled BF: A Graph 4738

10 nternatonal Journal of Appled Engneerng Research N Volume 13, Number 7 (2018) pp Research nda Publcatons. Based Fngerprnt Representaton and Matchng Algorthm, Advances n Bometrcs Lecture Notes n Computer cence Volume 3832, 2005, pp [29] hoba Dyre and C.P. umath, Fngerprnt Classfcaton based on Novel Drectonal Features usng Neural Network", nternatonal Journal of Computer Engneerng and Applcatons, Vol. 11(10), pp.1-13, [30] Heeseung Cho, Kyoungtaek Cho and Jahe Km, Fngerprnt Matchng ncorporatng Rdge Features wth Mnutae, EEE Transactons On nformaton Forenscs And ecurty, Vol.6(2), pp , [31] Jun La, We-YunYaub, HanWang, Combnng sngular ponts and orentaton mage nformaton for fngerprnt classfcaton", Pattern Recognton, Vol. 41(1), pp ,