Partial Fingerprint Matching

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1 Paral Fngerprn Machng Tsa-Yang Jea, Vraj S. Chavan, John K. Schneder and Venu Govndaraju Sae Unversy of New York a Buffalo Amhers, New York 14228, USA jea@cedar.buffalo.edu ABSTRACT Fngerprn denfcaon s a well-researched problem, and auomac fngerprn denfcaon/verfcaon echnques have been successfully adaped o boh cvlan and forensc applcaons for many years. However, hs echnology suffers from he problem of handlng ncomplee prns and ofen dscards any paral fngerprns obaned. Recen research has begun o delve no he problems of ncomplee or paral fngerprns. The nroducon of small chp-based slcon scanners ha capure only a par of he fngerprn area has made hs problem mporan from he commercal pon of vew. Prevously aemps have been made o solve he paral fngerprn problem by applcaon of varous core-based algnmen echnques before machng he paral prns, bu hs approach fals f he paral prn does no nclude he core. The novel approach presened here uses a new se of localzed feaures, called as secondary feaure, whch are consruced from he ANSI-NIST sandard mnua-based fngerprn represenaons, wh a mul-pass rangle-based machng algorhm desgned o work on hose secondary feaures by applyng he conceps of dynamc olerance areas. We also analyze he vulnerably of fngerprn denfcaon sysems desgned o work wh paral fngerprns o brue force aacks. In hs paper, we dscuss he hrea model n erms of b srengh of paral fngerprns. The underlyng heorecal conceps of our secury-based analyss and of our machng algorhm for paral fngerprns are explaned and he resuls of our expermens are presened. Keywords Paral fngerprn, b srengh, mnuae, fngerprn machng. 1. INTRODUCTION Over he las decade, compuer echnology has been successfully employed o boh capure and process fngerprn daa. Successful mplemenaon of such fngerprn denfcaon sysems ncludes he Auomac Fngerprn Idenfcaon Sysem (AFIS), whch currenly operaes n he FBI a Clarksburg, WV. Alhough, auomaed fngerprn denfcaon and verfcaon echnology has been wdely adoped n commercal and secury applcaons for access and denal operaons, he echnology also lends self o oher emergng areas of neres. One of he mos mporan areas of consderable uly value o he law enforcemen agences s ha of paral fngerprn machng. In many cases on crme scenes, paral fngerprns are obaned whch could be useful o denfy he crmnals. For example, he FBI manans a daabase of en prns, whch s he collecon of fngerprns of all en fngers of known crmnals or suspecs denfed a varous law enforcemen ceners. I would be of remendous use f he paral fngerprns could be used o mach agans such a daabase o oban a subse of poenal suspecs ou of he mllons of such records, hen hose mached cases can be more specfcally pursued (Fgure 1). Recenly, he adven of chp-based slcon scanners whch capure only a small regon of he fngerprn surface area has necessaed he sudy of machng algorhms whch do no depend on avalably of core or dela sngulares n he fngerprn mage. The problem of paral fngerprn machng, alhough ouwardly smlar o he ssue of complee fngerprn machng problem, harbors s own unque characerscs ha presen a hurdle o mplemenaon of such paral fngerprn denfcaon sysems. In hs paper, we look a he sublees of hs problem from mplemenaon pon of vew and propose a machng algorhm, whch can be used o address hs problem. Fgure 1. Laen Fngerprn Image (lef) wh s machng en-prn (rgh) [1]. 2. PARTIAL FINGERPRINTS Machng paral fngerprns o a pre-fled complee fngerprn s usually encounered n forensc applcaons. In many cases, he paral fngerprn mages ha lfed from crme scenes are broken and unclear. Thus, he useable pars of he paral fngerprn mages are resrced n small areas. Machng he small pars (paral) fngerprn o he pre-fled mages n daabase usually has he followng problems: The paral fngerprns obaned from a crme scene are normally small and nosy. The number of mnua pons avalable n such prns s less and furher reduces he dscrmnang power. Dffcul o asceran correspondence of obaned paral fngerprn o one of he fngers even f en-prns are avalable.

2 Loss of sngular pons (core and dela) s hghly lkely, so a robus algorhm ha s ndependen of relyng on hese sngulares s requred. algorhm, such as chancode-based [9] or hnnng-based [4] algorhm, hen beng appled on enhanced mages o exrac he mnua nformaon. Unconrolled mpresson envronmens resul n unspecfed orenaons of obaned paral fngerprns and also dsorons are nroduced due o characerscs of human skn such as elascy. (a) Bfurcaon (b) Rdge-endng Fgure 3. Examples of mnuae. Fgure 4. Some fngerprn enhancemen resuls from [1]. The mage lef s he orgnal npu mage and he rgh s he enhanced mage. We have developed a mnuae exracon algorhm usng chancode represenaon and are able o exrac by searchng he sgnfcan urns, and represen mnua n sandard NIST forma. Fgure 2. Challenges faced by an Auomac Fngerprn Idenfcaon Sysem (AFIS). Due o he unque characerscs presened by paral fngerprns, mplemenng a fngerprn denfcaon sysem based on paral fngerprns presens a greaer challenge han convenonal AFIS for complee fngerprn machng (Fgure 2). We have o focus on overcomng he problems we menoned above. 2.1 Feaure exracon In order o overcome he challenges ha a fngerprn denfcaon sysem based on paral fngerprns would face, we need o carefully selec he feaures we wan o use for paral fngerprns. Our sudy usng a specal feaure se ha s derved from mnua, shown n Fgure 3, based on fngerprn represenaon. I has several advanages: () mnua-based fngerprn represenaon s an ANSI-NIST sandard [3], () conans only local feaure nformaon bu does no rely on he fxed global nformaon such as sngular pons, or cener of mass of fngerprns. For a mnua-based model o be used, s mperave ha he paral fngerprn mage s subjec o varous mage enhancemen echnques o oban clearer rdge srucures. Good fngerprn mage enhancemen algorhms should have he ables o remove he background nose, locae and exrac he fngerprn rdge srucures fas and accuraely. Enhancemen echnques lke [1] [2] can be appled o oban a beer qualy fngerprn mage (Fgure 4). A suable mnua exracon (a) Fgure 5. (a) Mnuae locaon n chancode conours, (b) he dsance beween he hresholdng lne and he y-axs gves a hreshold for deermnng a sgnfcan urn [9]. 2.2 Mnua machng The paral mnua se obaned as a resul of feaure exracon sage s mached wh he se of mnua emplaes for pre-fled complee fngerprn mages. Mnua machng problem can be seen as a problem of pon paern machng, and a number of pon paern machng algorhms have been proposed [4] [5] [6]. To reduce exponenal number of searchng pahs, many of hese algorhms use feaures assocaed wh pons, and some oher spaal properes, such as relave dsance and rdge samples [4]. However here are several drawbacks when hese echnques are appled o he problem of paral fngerprn machng. For example, relaxaon approaches o pon paern machng echnques are slow due o her erave naure [5]. Hough ransform based pon paern machng [6] would fal on paral fngerprns because of small number of mnua pons. Usng genec algorhm and smulaed annealng o fnd a possble subopmal mach end o be very slow [5]. We propose a new mul- (b)

3 pass machng algorhm based on rangular machng and newly desgned secondary feaures, whch base only on mnuae nformaon, o overcome he varous problems menoned above. 3. VULNERABILITY OF PARTIAL FINGERPRINT IDENTIFICATION SYSTEMS In hs secon we assess he vulnerably of a fngerprn denfcaon sysem based on paral fngerprns o a brue force aack, whch s essenally he problem of conjurng up a se of paral emplaes conssng of mnua pons and machng hem agans a se of complee fngerprn emplaes. For Example, n a smar card sysem where he bomercs emplae s sored n he card and presened o he auhencaon sysem, a hacker could presen hese random ses o he auhencaon sysem assumng ha he hacker has no nformaon abou he sored emplaes [7]. The log 2 of he probably of randomly guessng a correc feaure se hrough a brue force aack referred o as b srengh and represens he equvalen number of bs n a password auhencaon sysem [7]. One of he nheren srenghs of fngerprn machng problem s ha a gven fngerprn mage has a hgh b srengh whch s comparable o srong password sysems. However, as he number of mnua avalable n query emplae decreases as n he case of paral fngerprns, he above assumpon s no longer vald. We assume ha he sysem uses a mnua-based machng mehod and he number of pared mnuae reflecs he degree of mach. We need o fnd he probably P ver ha a gven query emplae maches he reference emplae. Ths essenally means he cumulave probably of mnua above a fxed number h (11 n our analyss) n query emplae machng he reference fngerprn mnua. We use he followng noaons: N r denoes he number of mnua pons n he reference (complee) emplae, and N q denoes he number of mnua pons n he query emplae whch vares from 1% o 4% of he orgnal fngerprn mage mnua. Noe ha he percen of mage sze less han 4% conans less han mnuae. We sudy he mpac of varyng fngerprn mage szes on he ably of a paral fngerprn denfcaon sysem o a ress brue force aack amed a securng a false mach by presenng a randomly generaed se of mnua o he sysem. The analyss ams o denfy he facors ha nfluence he probably of a false mach. Followng he assumpons n he analyss by Raha [7], we represen a fngerprn mage sze I s = x y pxels where x and y represen he dmensons of he fngerprn mage. We assume he ner-rdge dfference (f) o be 15 pxels n he analyss. The maxmum possble mnua locaons (l) are 2 hus ( x y) / f. Along wh he posons avalable for he locaon of he mnua pons, we also need o asceran he number of allowed drecons (d) assocaed wh every mnua pon. We assume ha each randomly generaed mnua has a machng probably p = N /(( l N + 1) d). The probably p r q ( ) ha exacly randomly generaed mnua pons mach s N q gven by p. These random mnua pons can = p (1 p) be seleced from pons n r N ways. The probably s he r C N Pver cumulave probably of mnua pons machng from a fxed hreshold h o he maxmum number of avalable mnua pons n N q he emplae,.e. Nr N. The b q P ver = = C p h srengh of he sysem s hen calculaed as log 2 (1/ p ver ). We evaluae he b srengh of he paral fngerprn denfcaon sysem for dfferen values of d. B Srengh (n bs) Effec Of Im age Sze on B Srengh Im age Sze (%) d=4 d=8 d=16 d=32 d=36 Fgure 6. Effec on b srengh of fngerprn denfcaon sysem of decreasng npu mage sze. B Srengh s ploed n Fgure 6 wh mage sze gong from 1% o 4% of he reference mage sze for N r =51 and N q havng values from 11 o a maxmum of 51. The mage szes and he values of N r and N q used for he mahemacal analyss are he acual mage szes and he number of mnua n he reference and he query emplaes respecvely, used n he expermens wh our paral-machng algorhm. We fx he hreshold for mach as 11. A mnmum of four quanzaon levels provdes a 45 degree olerance, whle 16 levels provde roughly an 11 degree olerance [7]. I s assumed ha he macher wll olerae shfs beween query and reference mnuae of a mos a rdge and valley pxel wdh, and an angular dfference of up o half a quanzaon bn (±45 degrees for d = 4) [7]. The b srengh s evaluaed a d=4, 8, 16, 36 and 36. We make several mporan observaons. Whle desgnng paral fngerprn denfcaon sysems, he less number of mnua pons avalable a he npu as a resul of only a par of he fngerprn mage beng capured, lead o a naural degradaon of he b srengh equvalens of such sysems and so hese are more vulnerable o varous brue force aacks. Ths presens a poenal secury hrea o commercal mplemenaons of such sysems. Based on he mahemacal analyss oulned above, we recognze he need o develop a paral fngerprn denfcaon sysem whch s robus as well as capable of ressng a brue force aack (randomly generaed mnua pons se beng recognzed as a vald se) n he case of paral fngerprns where such occurrences are very lkely. For paral fngerprn machng, as fngerprn sze changes, oher facors remanng consan, we need o ncrease d o overcome he drop n b srengh as demonsraed above. Of course, f every mnua

4 Table 1. Effec on b srengh of fngerprn denfcaon sysem of decreasng npu mage sze. % of Image Sze Image Sze X Y N q d=4 d=8 d=16 d=36 d= was allowed o have an ndependen orenaon of any of 36 degrees, maxmum b srengh s observed bu would obvously make he machng problem mpossble o be solved, consderng he varous dsorons ha are nroduced when fngerprn mages are capured and hence s only useful from he heorecal pon of vew. Whle desgnng a fngerprn denfcaon sysem ha works only on a subse of he oal avalable mnua pons recovered from paral fngerprn mages, we have o selec an approprae value of d so ha he paral fngerprn denfcaon sysem has a hgh b srengh whch makes secure agans brue force aacks as oulned above. Ths observaon also leads o an mporan concluson ha we need o assocae more deal wh he localzed feaure represenaon n a mnua-based approach o decrease he hrea of a brue force aack based on presenng These resuls also help esablsh crera o deermne he mnmum sze of he fngerprn mages ha can be relably handled by he fngerprn denfcaon sysem. For example, n vew of he above assumpons, for a fxed quanzaon level, low b srengh would sugges ha however good he machng algorhm may be, such sysems can be easly compromsed by a brue force aack as oulned n he above scenaro. We can hen use hese resuls o dscard such fngerprn mages before he machng process self or assocae some addonal nformaon wh he paral fngerprn, whch can be used n conjuncon wh he machng resul o asceran s deny. Ths assumpon s based on only a sngle feaure based machng and so f we can assocae more deal wh he local feaures we can acheve beer secury agans such aacks. random se of mnua o he sysem. We also sudy he effec of sze of npu mage as follows. # Mnua Obaned No. Of Mnua Obaned n Query Templae vs Image Sze 5 Image Sze n % No. Of Mnua n Query Templae 1 Fgure 7. No. Of Mnua Obaned n Query Templae agans he Percenage of he oal mage sze. We also observed ha he number of mnua ha we were able o exrac from he fngerprn mages decreased sharply as he sze of he npu mage was decreased. From Table 1, we observe ha a 4% of he orgnal mage sze we obaned b srenghs of, 3.3, and 27.3 for d=4, 8, 16 and 32 respecvely. Based on hese values, we can develop a measure of he percenage of npu mage sze based on he number of mnua exraced n he query emplae whch can provde he mnmum level of secury agans brue force aacks and hus selec a mnmum quanzaon level. 4. MULTI-PASS MATCHING ALGORITHM In hs secon, we presen our machng algorhm, whch assocaes new deal wh he localzed feaures n he form of secondary feaures. Our algorhm overcomes he challenges presened by paral fngerprns, such as roaon varaons and he geomerc dsorons ha we menoned above, by ulzng he pure localzed feaures and a unque mul-pass machng algorhm o acheve he our goal. We explan he algorhm as follows: 4.1 Generang secondary feaures One of he bgges challenges n mnuae-based fngerprn machng algorhm s o handle he dsorons of mnuae posons, orenaons ha are caused by dfferen mpressng pressures, dfferen shear forces, ec. All hs ransformaons could be boh global-wsed and local-wsed, and boh lnear and non-lnear. Moreover, we need o mach an ncomplee (paral) fngerprn o a complee fngerprn. In order o perform more relable and effcen machng on no so resrced fngerprn mages, e.g. unresrced orenaons and ceran dsorons, we proposed a se of new feaures based on he mnuae represenaon. We refer he new se of feaures as secondary feaures. Snce he ANSI-NIST sandard represenaon, whch s mnuae-based, s wdely used by many auomac fngerprn verfcaon/denfcaon sysems, we generae our secondary feaures by only usng ha nformaon, hus our approach could be easly adaped o oher sysems. Secondary feaures are orenaon nvaran and pure localzed feaures. Orenaon nvaran means we do no need o care abou he dfference of fngerprn orenaons beween he es and emplae mages. Localzed feaures have he advanages of makng he geomerc

5 Fgure 8. Ten percen dsoron on each small rangle can lead o huge global dsoron [8]. dsorons easer o be conrolled and beng applcable even f only paral fngerprns are avalable. Global deformaons are usually larger han local deformaons. A small amoun of local dsoron can lead o relave large global dsoron. In [8], here s a very good llusraon abou hs concep (Fgure 8). In order o generae a secondary feaure ha s orenaon nvaran, he spaal coordnaes, whch are embedded n he mnuae-based fngerprn represenaon, wh respec o a fxed orgn pon, usually s he op-lef corner of he mage, canno be used. The coordnaes mgh be very dfferen, even he wo fngerprns are from he same fnger bu capured wh dfferen orenaons or poson dsplacemens. Especally for paral fngerprns, hey are only a small par of a fngerprn and we do no have knowledge abou her locaons and orenaons. Some sysems ry o conver he coordnaes o a new coordnae sysem, whch orgnaes a a sngular pon (.e. core or dela) n he fngerprn mage. I s obvously no a proper dea, snce no all fngerprn mages have he sngular pon n especally when we handle he paral fngerprns. Assumng ha all he mnuae are sored by he x and y coordnaes. Le M (,, x y θ ) be he h mnua wh spaal coordnaes ( x and orenaon, y ) θ n he fngerprn mage, and (,, θ ), (,, θ ) be he wo neares N xn yn N x yn 1 N1 mnuae of M n erms of he Eucldean dsance beween mnuae. S ( r, r 1, φ, φ 1, δ ) denoes a secondary feaure where he s he lengh of he frs leg of he acue angle when we r race he angle clockwse, and r 1 s he lengh of he oher leg of he angle. δ s he angle beween he wo sdes (Fgure 9). Whle φ denoes he orenaon dfferences beween N and M (.e. θ ), and φ1 represens he orenaon dfferences for N θ and M. We compue for each n he ranng sage (offlne process) and hen sore hese new generaed secondary S M feaures no an nernal daabase as emplaes. In addon, we sor hese secondary feaures accordng o he values of her elemens. When we machng wo secondary feaures, we frs verfy he wo orenaon dfferences, and hen check f he values of r,, and r 1 δ beween he query and reference secondary feaures exceed some pre-defned hresholds, we could jus skp he reference secondary feaure pon assocaed o he value and go on o he nex pon. Because he daabase s sored accordng o he values of her secondary feaure elemens (.e. r, r, φ, φ, and δ ), we can know ha beyond a ceran secondary 1 1 feaure pon, he feaure dsance wll always exceed he desred hreshold. In ha case, we do no need o mach all he n each reference emplae. Ths would grealy reduce he machng me for wo fngerprns. r 1 M Fgure 9. Secondary feaure for M. M r δ r r N, and s he lengh of he sde 1 N S s he lengh of he sde M. δ s he acue angle beween he wo sdes. Where N M N M Secondary feaure machng Mos mnuae-based machng algorhms, e.g. n [4], perform he machng by algnng he query and reference fngerprns frs, hen ryng o fnd one-o-one correspondences for every mnuae. The proposed rangle-based mul-pass machng algorhm has

6 he advanage ha we do no need o algn he fngerprns frs. We could oban a ls of canddae fngerprns and he query and reference fngerprns are algned durng he machng process. Whle he algnmen problem beween fngerprns s aken care by he secondary feaure, we sll need o handle he specal dsorons. Consder mpressng he surface of a fnger ono o a paper or he plaen of a lve fngerprn scanner; fngerprn s mapped from a 3-dmenson space ono a 2-dmenson space, hus here mus be ceran degree of dsorons caused by dfferen vercal pressures, shear forces, and mpresson condons on every me we oban fngerprns. By generang he hresholds dynamcally accordng o he dsance beween cener mnua and s neghbor (.e. leg lengh), we can make he dsorons much easer o be handled. We observed ha he dsorons of angle and orenaon are more sgnfcan when he leg lengh s larger and vce versa, whle he local dsorons n dsances are smaller han he global dsorons. I s reasonable o make he hresholds for shorer legs o be more resrced n dsances han he hresholds for longer legs of he secondary feaure. A he mean me, he hresholds for angles should be more resrc for longer legs bu more flexble for shorer legs. All hese dynamcally generaed hresholds consruc he flexble olerance areas (Fgure 1). fngerprn, we would have a ls of secondary feaures on boh query and reference fngerprns. In he ls, some of he mached feaures are false-mached. Those false-mached feaures are caused by he localzed secondary feaures. Thus, we need o use some more larger-scale nformaon, such as he relaons beween mached feaures, o fler he false mached feaures ou. A valdaon sep (or posprocessng) on he mached secondary feaure pars ls s nroduced. To ulze he global feaures o elmnae he false posves and o oban beer esmaes, we calculae orenaon dfferences (ODs) whch s essenally he dfferences beween he orenaons of secondary feaures. We dvde hese ODs no 36 bns of 1 degrees each. We denfy he bn wh maxmum number of canddaes (secondary feaure represenaons) hen consruc he secondary feaures from he feaures n ha bn. Then repea he algorhm agan for hese new generaed secondary feaures, whch conans he more globalwse nformaon beween canddaes, ll a sasfacory subse s found. The exac pon of soppng he search s sll a creron for our research and depends sgnfcanly on he naure of he applcaon and also on he desred accuracy and he accepable compuaonal cos. Usually wo passes should be suffcen for mos applcaons. The man dea of he algorhm s o begn he machng wh local regon hen res o valdae hose poenal mached par from a more global pon of vew. M r N Thld r 1 Thld 1 5. PRELIMINARY RESULTS Expermens were performed on lve-scanned fngerprn mages obaned from Ulra-Scan daabases Sum95_1, Sum95_2, and Sum95_3. Because ha our research focuses on he paral fngerprns and hey are mos used n denfcaon applcaons, our expermens are all esed n an denfcaon approach. The algorhm was appled o varyng mage szes from 1% o 6%. The ess were carred ou for boh he op choce as well as for op N-canddaes (op 2 choces) o compare he performance of he algorhm. For mage sze greaer han 7%, op 7 choces suffce o nclude he correc choce (Fgure 11) Fgure 1. The gray areas are he olerance areas, whch defned by dynamc hresholds, of. S Assume S and S are he secondary feaures ha we wan o j mach on query and reference fngerprns respecvely. We say S and are mached when he wo neghbor mnuae S j N, and N boh fall no he olerance areas of and 1 S φ φ < Thld ( = φ, 1 and Thld are dynamcally defned hresholds). By hs, we φ can fnd a ls of mached canddae secondary feaures for he query secondary feaure. From hs mached canddae feaure ls, we fnd one s he bes-f n hose mached feaures by seleced he one wh lowes feaure dsance. The feaure dsance s a merc ha calculaed by mached query and reference secondary feaures. The feaure dsance s also a research opc for he fuure, snce has very grea mpac o he algorhm performance. Afer all he eraons on he query Sze Of FngerPrn(%) No. Of Canddaes 6% 7% 8% 9% 1% Fgure 11. Performance on paral fngerprns. As seen from he above graphs we can conclude ha as mage sze decreases he sysem performance also decreases. The algorhm

7 gves a correc rae of 76% for mages 6% of he orgnal sze for op 2 choces and 92% for op 2 choces for 1% sze. The denfcaon performance for op choce whou hreshold echnque s as shown below. Performance (%) Image Sze (%) Correc Rae Error Rae Fgure 12. Sysem performance whou hreshold echnque. The denfcaon performance for op 2 choces whou hreshold echnque was observed as follows (Fgure 13). The reason of analyzng he op 2 resuls s ryng o undersand he poenal of he machng algorhm, hus we can furher fne une he parameers n he fuure. Performance (%) Image Sze (%) Correc Rae Error Rae Fgure 13. Sysem performance for op 2 choces whou hreshold echnque. 6. SUMMARY and FUTURE WORK Auomaed paral fngerprn denfcaon s a problem ha has ye o be adequaely solved. We have addressed he problem a wo levels. The heorecal analyss draws a relaon beween varaon n he szes of he npu paral fngerprn mage and he b srengh of paral fngerprn denfcaon sysems. I also res o quanfy he creron for a hreshold for he mnmum sze of fngerprn mage ha can be mached relably wh adequae secury agans brue force aacks. Our machng algorhm overcomes he drawbacks of convenonal approaches o paral fngerprn machng by usng a new novel mul-pass approach usng secondary feaures. Our secondary feaures and machng algorhm have he followng advanages: () secondary feaures are generaed from mnuae nformaon, so our approach can be easly adaped n many applcaons; () secondary feaures are nvaran o orenaons, hs overcomes one of he bgges challenges n paral fngerprn machng; () localzed feaures and dynamc olerance areas provde he power o handle he specal dsorons; (v) our machng algorhm s suable for paral fngerprn denfcaon, snce begns wh a local pon of vew hen valdaes he resuls by usng global relaons beween secondary feaures. We also noced ha here s sll a lo of poenal for mprovemen of he sysem performance. We wll focus on nvesgang he deermnaon of dynamc hresholds, he generang of feaure dsances, and mprovng he machng processes n erms of speed and accuracy. We wll also esablsh a paral fngerprn daabase for sysem developmen and performance comparsons. 7. ACKNOWLEDGEMENTS Ths We graefully secon wll acknowledge be added n our camera-ready many useful paper dscussons wh Dr. Wen-Jann Yang and Dr. Zhxn Sh of he Cener of Excellence for Documen Analyss and Recognon (CEDAR), Amhers New York. The hgh qualy lve-scanned fngerprns, whch are colleced va hgh-qualy ulrasound fngerprn scanner, are obaned from Ulra-Scan Corporaon, Amhers, New York. Fgure 1 s aken from [1]. Fgure 4 s aken from [1]. Fgure 5 s aken from [9]. Fgure 8 s aken from [8]. 8. REFERENCES [1] Ln Hong, Fngerprn Image Enhancemen: Algorhm and Performance Evaluaon. IEEE Transacon on Paern Analyss and Machne Inellgence, Vol. 2, No. 8 (Augus 1998), [2] Shlomo Greenberg, Mayer Aladjem, and Danel Kogan, Fngerprn Image Enhancemen usng Flerng Technques. Real-Tme Imagng 8 (22), [3] Amercan Naonal Sandard for Informaon Sysems Daa Forma for he Inerchange of Fngerprn Informaon. Doc# ANSI/NIST-CSL , Amercan Naonal Sandards Insue, New York, [4] Anl K. Jan, Ln Hong, Sharah Pankan, Ruud Bolle, An Ideny-Auhencaon Sysem Usng Fngerprns. Proceedngs of he IEEE, Vol. 85, NO. 9 (Sep. 1997), [5] J. Ton and A. K. Jan, Regserng landsa mages by pon machng. IEEE Trans. Geosc. Remoe Sensng, Vol. 27, No. 5 (1989), [6] D. H. Ballard, Generalzed Hough ransform o deec arbrary paerns. IEEE Transacons on Paern Analyss and Machne Inellgence, No. 2 (181), [7] Naln K. Raha, Jonahan H. Connell, and Ruud M. Bolle. An Analyss of Mnuae Machng Srengh. [8] Zsol Mklós Kovács-Vajna, A Fngerprn Verfcaon Sysem Based on Trangular Machng and Dynamc Tme

8 Warpng. IEEE Transacons on Paern Analyss and Machne Inellgence, Vol. 22, No. 11 (Nov. 2), [9] V. Govndaraju, Z. Sh and J. Schneder, Feaure Exracon Usng a Chancoded Represenaon of Fngerprn Images. hp:// (23). [1] NIST Specal Daabase 27 Fngerprn Mnuae from Laen and Machng Tenprn Images