Fan Filtri - Prepoznavanje Otiska

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  • 8/8/2019 Fan Filtri - Prepoznavanje Otiska

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    Eng. Rev. 28-1 (2008) 39-50 ____________________________________________________________________________________________________________________________________________________

    UDK 004.932

    USMJERENI DIGITALNI FILTRI ZA PREPOZNAVANJE OTISAKAPRSTIJU

    DIRECTIONAL DIGITAL FILTERS FOR FINGERPRINT RECOGNITION

    Goran BORKOVI - Miroslav VRANKI Viktor SUI

    Saetak: U ovome radu, analizirali smo slike otisaka prstiju koritenjem usmjerenih digitalnih filtara. Rije je o

    filtrima klinastoga oblika podru ja proputanja, tzv. fan filtri. Primjenom takvih filtara razvijena je metoda

    odreivanja usmjerenosti papilarnih linija slike otiska prsta u svrhu odreivanja podudarnosti dvaju otisaka. Dobiveni

    rezultati potvruju pozitivna oekivanja o primjeni fan filtara u podruju biometrije.

    Kljune rijei: papilarne linijefan filtarotisak prstadominantna usmjerenost

    Abstract: In this paper we have analyzed fingerprint images using directional digital filters. The filters have a double

    wedge-shaped pass band, hence the name fan filters. Using these filters, a method is developed for determining the

    ridge orientation for the purpose of comparing two fingerprints. The obtained results confirm the applicability of fan

    filters in biometric applications.

    Keywords: ridgesfan filter

    fingerprintdominant orientation

    1. INTRODUCTION

    In this paper we analyze a digital fingerprint image byusing directional digital filters. These filters have adouble wedge-shaped pass band, as shown in Figure 2.By using such filters, a method for determination of ridgedirectionality in fingerprints is developed, which allowsfor the comparison of two different fingerprint imagesthat originate from the same finger. An appropriatealgorithm is developed using MATLAB R2006b.

    Gabor filters [1], [2], [3], and a tree of directional filterbank based filters (DFB) [4] are frequently used in theanalysis of fingerprints. In this paper, however, we haveused fan filters, with three parameters only (order, angleand rotation angle), which are much simpler and easierto apply and processing time is shorter than either theGabor or the DFB filters. Another motivation for the useof fan filters in dactiloscopy has been their limited use insuch applications.

    The human skin is divided into two layers; the outer andthe inner layer. The outer layer is known as theepidermis, while the layer under the epidermis is alsoknown as the dermis.

    1. UVOD

    Rad se bavi analiziranjem digitalnih slika otisaka prstijukoritenjem usmjerenih digitalnih filtara. Rije je ofiltrima klinastoga oblika podru ja proputanja, tzv. fanfiltri (Slika 2). Na temelju takvih filtara razvijena jemetoda odreivanja usmjerenosti papilarnih linija slikeotiska prsta u svrhu odreivanja podudarnosti dvajurazliitih slika otiska koje potjeu od istoga prsta isteruke. Algoritam je izraen koristei programski paket

    MATLAB R2006b.

    Kao digitalni filtri za estimaciju orijentacije u brojnimradovima koriste se Gabor filtri [1], [2] i [3], dok se u [4]koristi usmjereni filtarski slog. U ovom radu se kodanalize papilarnih linija upotrebljavaju fan filtri, koji susa svoja tri parametra (red, kut i zakret) jednostavniji odGaborovih te DFB-a i laki za primijeniti te trose i manjeraunalnih resursa. Oni su ujedno nedovoljno iskoriteniu praksi, pri analizi otisaka, pa to predstavlja i dodatnimotiv u njihovoj to boljoj implementaciji u podrujudaktiloskopije.

    Koa se dijeli na vanjski dio (epidermu) i unutarnjesluzne slojeve. Prsti, dlanovi te donji dijelovi epiderme

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    40 G.Borkovi, M.Vranki, V.Sui: Usmjereni digitalni filtri____________________________________________________________________________________________________________________________________________________

    na stopalima ispresijecani su mnogobrojnim brjegovima idolinama koji tvore papilarne linije. One se poinjustvarati u maternici, tijekom prvih tjedana razvoja fetusa[5] .

    Otisak papilarnih linija je jedinstven, a metodaidentifikacije otiskom prsta se temelji na dvije glavnepremise [6]:

    glavne karakteristike otiska prsta su vremenskinepromjenjive,

    otisak je jedinstven za svakoga pojedinca to je opeprihvaeno na temelju empirijskih rezultata.

    Karakteristike slike otiska se mogu podijeliti na lokalne iglobalne. Globalne obuhvaaju tok papilarnih linija kojetvore neki od prepoznatljivih oblika (Slika 1) po kojimase otisci svrstavaju na pet klasa (lijeva i desna petlja, luk iatorski luk te spirala) [7]. Globalne karakteristike nisudovoljne za pouzdanu identifikaciju.

    Papilarne linije su ponekad vrlo kratke (toka) ili iz jednenastaju dvije (bifurkacija, grananje), odnosno obilujuraznim detaljima koje se nazivaju minucije (lokalnekarakteristike). One jesu dovoljne za pouzdanuidentifikaciju, ali sustavi za prepoznavanje, temeljenisamo na njima ([8] i [9]), zanemaruju ostaleedinstvenosti otiska kao to je primjerice lokalna

    orijentacija papilarnih linija. Estimacija lokalnedominantne orijentacije je jedna od najvanijih operacijau sustavima za automatsko prepoznavanje otisaka prstiju

    [10].

    Poetci raunalne ere, odnosno pojavom sklopovlja kojemoe podrati automatski sustav za prepoznavanjeotisaka AFIS, okrenuta je nova stranica u ovome dijelu biometrije. Krajem 70-tih i poetkom 80-tih godina 20.stoljea, policije u Kanadi i San Franciscu (SAD) uvodeprve takve sustave. Danas AFIS sustave dijelimo premanjihovoj primjeni na forenziku, civilnu i komercijalnu[11].

    Slika 1. Pet kategorija otisaka; s lijeva na desno: lijeva petlja, spirala, atorski luk, desna petlja i luk

    Figure 1. Five categories of fingerprints; from left to right: left loop, whorl, tented arch, right loop and arch

    Depending on the surface considered, we generally referto parts of epidermis as fingerprints, palmprints, andsoleprints. They are made of a series of friction ridges,taking various forms and shapes. These ridges aredeveloped during the first weeks of human gestation [5].

    Fingerprints are unique, and their identification is basedon two premises [6]:the basic characteristics of fingerprints do not change

    over time, andthe fingerprint is unique to an individual (the finding

    is based on empirical results).

    The characteristics of a fingerprint are divided into twogroups, namely, local and global characteristics. Thelatter are represented by a flow of ridges that formdistinctive shapes (Figure 1), by which we can definefive classes of fingerprints (left and right loop, arch,tented arch and whorl). The global characteristics aloneare not sufficient for positive identification.

    Sometimes, the ridges are very short (dot), have branches(bifurcation), or could have some other minutiae.Minutiae details are sufficient for positive identification,but identification systems based on them solely ([8] and[9]) do not take into consideration other unique features,like the local ridge orientation. The estimation of thelocal dominate orientation is one of the most importantoperations in systems for automatic fingerprintidentification [10].

    The introduction of computers that can support automaticfingerprint identification systems (AFIS), represented animportant development step in the field of biometrics. Bythe end of 1970s and the beginning of the 1980s, the lawenforcement agencies in Canada and USA implementedthe first AFIS systems. Today, AFIS systems areclassified as forensics, civil, or commercial application-orientated AFIS systems [11].

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    (1)

    (2)

    2. ANALIZA OTISKA PRSTIJU

    Analiza slike otisaka prsta izvodi se dvodimenzionalnimFIR filtrima nulte faze i klinastoga oblika podruja

    proputanja, tzv. fan filtri (eng. fan = ventilator). Onisvojim rotiranjem propusnog podru ja u frekvencijskojdomeni za neki zakret oko ishodita (toka simetrije dvaklina), proputaju prostorne frekvencije signala te ovisnoo najveoj sumi proputenih piksela za neki kut dolazi sedo dominantne usmjerenosti sadraja slike ili dijelovaslike koja se koristi u analizi. Tijekom analize koriten jefan filtar 31. reda te irine propusnog podru ja (kut) od15 (za traenje referentne toke) ili 20 (zakompenzaciju rotacije i zavrnu usporedbu).

    Slika 2. 3-D prikaz fan filtra nieg (lijevo) i vieg reda (desno) istih kuteva i razliitog zakreta

    Figure 2. 3-D display of a low order (left) and a high order fan filter with same angle and different rotationDvodimenzionalna konvolucija slike te impulsnogaodziva fan filtra h za svaki kut zakreta daje filtriranuslikuy:

    Mjera usmjerenosti za kut zakreta fan filtra (filtar reda31 i kut 15) dobije se kao suma apsolutnih vrijednostipiksela S() filtrirane slike:

    Kut zakreta , za koju je dobivena najvea suma, jeujedno i dominantni kut orijentacije.

    Na Slici 3 je prikazana konvolucija dijela slike otiska iimpulsnoga odziva filtra razlicitih zakreta. Suma piksela,odnosno elemenata ovih 9 matrica (slika u MATLABu jematrica) je razliita, a najvea suma ce biti ona kod kojee filtar najbolje propustio signal odnosno u ovom sluaju

    za kut zakreta od 40 (Slika 3; gore desno). Sad se dobiladominantna orijentacija, tj. usmjerenost papilarnih linija.

    2. FINGERPRINT ANALYSIS

    Two-dimensional zero phase FIR filters with doublewedge-shaped pass band, or fan filters (Figure 2), will beused in the fingerprint analysis. These filters, by rotatingtheir pass band in the frequency domain by an anglearound the starting point (the point of symmetry); filterout the space frequencies of a signal (which is made ofpixels). The largest sum of pixels in the pass band, for agiven angle, defines the dominant orientation of theimage content. During the analysis, a fan filter of the 21st

    order and a 15 (for seeking point of reference) or a 20(for the compensation of rotation and final comparison)width of pass band (angle) was used.

    Two-dimensional convolution of an image x and theimpulse response h of a fan filter, for every rotationalangle, produces the filtered imagey:

    The dominant orientation for the rotational angle isacquired as a sum of absolute values of pixels S() of thefiltered image:

    The rotational angle, for which the dominant orientation isdetermined, represents the dominant rotational angle.

    Figure 3 represents the convolution of an image (part of afingerprint) and impulse response of a fan filter withdifferent rotations. The sum of pixels or the matrixelements (image in MATLAB is a matrix) is different foreach of the 9 convolutions. The highest sum will bewhere the signal is filtered the most (Figure 3; upper righthand corner). Now we have the dominate orientation of

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    Tijekom analize slika otiska1 i otiska2 (Slika 4) istie senekoliko zasebnih koraka:predobrada (unsharp masking)pronalazak referentne toke i odabir podruja interesa

    (ROI) oko nje te kompenzacija rotacijeanaliza ROI-a te izraun slinosti dvaju otisaka

    Slika 4. Razliite slike istog otiska koje se analiziraju; otisak1 (lijevo) i otisak2 (desno)

    Figure 4. Different images of the same fingerprint which are analyzed; fingerprint1 (left) and fingerprit2 (right)

    2.1. Predobrada

    Predobrada, odnosno poboljanje kvalitete slike, sepomou fan filtara nije izvodila, ve je na slike otisaka primijenjena metoda filtriranja koja se naziva unsharpmasking(Slika 5). S njom se iz slike sa brojnim razinamasivila dobije crnobijela slika s istaknutim papilarama.Postupak se sastoji od zamuivanja kopije originalneslike koja se zatim usporeuje sa originalom. Ako jerazlika vea od postavljenog praga slike se oduzimaju, akao rezultat dobiva se otrija slika u odnosu na original. Na svim slikama otisaka je u programu Photoshopobavljen unsharp masking koliine 500%, radijusa 6.5 piksela te praga od 0. Te vrijednosti su odabraneeksperimentalno da bi se dobio to bolji kontrast bezgubitka vrijednih informacija na slici.

    ridges in an image.During the analysis of fingerprint1 and fingerprint2images (Figure 4) the next steps are followed:fingerprint enhancment (unsharp masking),finding the reference point and the region of interest

    (ROI) around it,ROI analysis and calculation of similarity.

    2.1. Fingerprint enhancment

    Fingerprint enhancement is performed in Photoshopusing the unsharp masking method (Figure 5). Ittransforms an image with numerous shades of gray intoan almost black and white image with more emphasizedridges than in the original image. Unsharp maskingconsists of applying a blur to a copy of the originalimage, and then comparing it to the original. If thedifference is greater than a user-specified threshold, theimages are subtracted. The resultant image is muchsharper than the original. On all of the analyzed imagesin this paper, 500% unsharp masking was used, with a6.5 pixel radius and 0 threshold. These values were

    chosen experimentally, and they result in images with thebest contrast and a minimal loss of information.

    Slika 3. Razliite slike istog otiska koje se analiziraju; otisak1 (lijevo) i otisak2 (desno)

    Figure 3. Different images of the same fingerprint which are analyzed; fingerprint1 (left) and fingerprit2 (right)

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    Slika 5. Slika otiska prije unsharp maskinga (lijevo) i nakon (desno)

    Figure 5. Fingerprint image before unsharp masking (left) and after (right)

    2.2. Referentna toka

    Slika otiska prsta (Slika 4) se dijeli na 8x8 dijelova,odnosno 64 kvadratia (Slika 6) te se svakome od njihnalazi dominantni kut usmjerenosti (Slika 7). Signal,

    odnosno sume apsolutnih vrijednosti piksela na grafu saslike 7, koje filtar proputa za neki kut zakreta, su radi preglednosti pretvoreni u postotke pri emu jedominantna usmjerenost uvijek ima sumu piksela od100%. Sumu piksela ostalih kutova koji nisu dominantni,zajedno sa onim kutom koji jest, nazvati emo sumomusmjerenosti, a ona je razliita za svaku sliicu.

    Slika 6. Podjela slike otiska na 64 dijela

    Figure 6. Division of a fingerprint image into 64 parts

    Zbog veliine ROI-a analiziraju se samo sumeusmjerenosti unutarnjih podjeljaka (Slika 6; isprekidaniokvir) jer u sluaju da rubna sliica sadri referentnutoku, onda ROI ili po visini ili po irini nebi bio kvadrat

    veliine 5/64 x 5/64 (Slika 6; podjela na 64 dijela).Unutarnje sliice su one koje do najblieg ruba slikeimaju najmanje neke dvije druge sliice.

    2.2. Point of reference

    The image of a fingerprint (Figure 4) is divided into 8x8parts, a total of 64 squares (Figure 6). For every square, adominant orientation is found (Figure 7). Absolute sums

    of pixels in Figure 6 are represented in percentages, withthe dominant orientation corresponding to 100%. Thesum of pixels for all the angles (dominant and non-dominant ones) is the orientation sum, and it is differentfor every square.

    Because of the ROIs size, the orientation sums of theinner squares (Figure 6; inside the dashed lines) are onlyconsidered. In the case that the outer square is chosensuch that it contains the point of reference, the ROI

    would not be a square of 5/64 x 5/64 dimensions (Figure6). So, the inner squares are those that have at least twoother squares between them and the image edge.Three squares with the most erratic ridges (29, 37 and 46)

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    Unutar tri sliice sa najnepravilnijim papilarnim linijama(29, 37 i 46) e se nalaziti referentna toka, odnosnosredite ROI-a. Kriterij za odabir jedne od njih tri uzimau obzir dominantne usmjerenosti susjednih sliica. Uidealnom sluaju referentna toka je jednaka sredinjojtoki (sredite najsjevernije papilarne linije oblika petlje),a sredinja toka se nalazi unutar podru ja najveezakrivljenosti papilarnih linija. Dakle, ako susjedi (Slika8; svijetlo siva polja) promatrane sliice (tamno sivo polje), imaju takvu dominantnu orijentaciju da je

    okruuju, onda to znai da je ona u sreditu nekakvogzakrivljenog podruja.

    Slika 7. Graf kutova usmjerenosti (x-os = kut []; y-os = sumacija piksela [%])

    Figure 7. Orientation angles graph (x-axis = angle []; y-axis = sum of pixels [%])

    will contain the point of reference. The criteria forchoosing one of them takes into account the dominantorientations of the neighboring squares. Ideally, thereference point is equivalent to the core point(corresponds to the center of the north most loop typesingularity), which is located in the center of the mostcurved area. If the neighboring squares (Figure 8; lightgray) have dominant angles that encircle the dark graysquare, then the dark gray square will be in the center ofa curved area.

    The orientation sum for orientations other than thedominant one will be higher, with the ridges being morecurved and erratic. Three such squares are the squares 29,37 and 46 (Figure 6; black frames). On the other hand,the ridges in the square 39 are two-dimensional sinusoid-

    like curves. For this square, the dominant angle (165)clearly dominates over the other angles (Figure 7; lowerleft corner).

    to su papilarne linije zakrivljenije i nepravilnije to eudio ostalih usmjerenosti biti vei. Tri takve sliice su broj 29, 37 i 46 (Slika 6; omeeno punom linijom). Za primjer je dan i graf usmjerenosti rubne sliice broj 39iji smjer papilarnih linija moemo zamisliti kao

    dvodimenzionalnu sinusoidu. Na Slici 7, dolje lijevo,vidljivo je da kut od 165, u sliici 39, uvjerljivodominira grafom za razliku od ostalih.

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    Slika 8. Vektori usmjerenosti

    Figure 8. Orientation vectors

    Slika 9. Referentna toka i ROI oko nje

    Figure 9. Point of reference and ROI around it

    U idealnom sluaju lijeva i desna susjedna sliica suednake kao sredinja (razlika apsolutnih vrijednosti

    dominantih kuteva je 0 izmedju njih), a gornja i donja supod kutom od 90 u odnosu na onu u sredini.

    Konano, oko referentne toke se odabire kvadratnopodruje veliine 5/64 x 5/64 to je i prikazano na slici 9.ROI drugoga otiska se pronalazi na isti nain.

    Ideally, the left and right squares have the sameorientation as the center one (the difference in theabsolute values of dominate angles between them is 0)and the orientation of the upper or lower squares are at90 to the orientation of the central square.

    Finally, around the reference point a squared ROI ischosen with 5/64 x 5/64 in size (Figure 9). The ROI ofthe second fingerprint is selected in the same way.

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    2.3. Kompenzacija rotacije

    Nakon odreivanja ROI-a oba otiska, analizira seglobalna usmjerenost njihovih papilarnih linija. Na Slici4 vidljivo je da je ROI drugoga otiska zarotirano usmjeru suprotnom od kazaljke sata u odnosu na ROI prvoga otiska. Potrebu za kompenziranjem rotacijepotvruje i dominantna usmjerenost dvaju ROI-a kodkojih jedan vektor pokazuje na kut od 40, a drugi na120 u odnosu na isprekidanu os (Slika 10).

    Na temelju jednoga kuta, ne moe se sa sigurnou rei ukojem e smjeru trebati zarotirati otisak2. Radi toga ROIdrugoga otiska se dijeli na etiri podjeljka (Slika 10) te sesvakome od njih odreuje usmjerenost. Vei broj kutova,odnosno podjela, nije poeljan jer se s time gubi naglobalnosti smjera papilarnih linija odnosno ne dobiva se

    orijentacija cijele slike otiska nego samo manjeg dijela.

    Ukoliko je veina kutova otiska2 vea od kutova otiska1znai da je otisak2 potrebno za odreeni kut zarotirati usmjeru suprotnom od kazaljke na satu i obrnuto. Ovisnoo veliini te razlike, za rotiranje se odabiru kutovi od10 ili 20. Ova dva kuta su odabranaeksperimentiranjem sa odreenim brojem otisaka iz bazepri emu su sa ovim vrijednostima kutova dobiveninajbolji rezultati. Za kut od -10 rotira se cijela slikaotiska2 te se zatim ponovno trai referentna toka, tj.ROI. Rezultat rotiranja prikazan je na Slici 12.

    Slika 10. Dominantne usmjerenosti vezane za kompenzaciju rotacije

    Figure 10. Dominate orientations for compensation of rotation

    2.3. Rotation compensation

    After the ROIs of the fingerprints are selected, the globalorientation of the ridges is analyzed. It can be seen inFigure 4 that the ROI of the second fingerprint is slightly

    rotated, in the direction opposite to the ROI of the firstfingerprint. Hence, the dominant orientations of the twoROIs are different, with one having the vector pointingtowards 40 and the other towards 120 with respect tothe dashed line (Figure 10).

    Based on the information of one angle only, it cant be precisely determined in which direction fingerprint2should be rotated. So, the ROI of the second fingerprintis divided into four parts (Figure 10) and for each part thedominant orientation is calculated. More than four partsis not desirable, because the global ridge orientation

    information would be lost (we would get a localorientation of a small part of a fingerprint).

    If most of the angels from the four squares offingerprint2 are larger than those of fingerprint1, then therotation in the anticlockwise direction is required, andvice versa. Depending on the size of the differencebetween the two orientations, the angles of size 10 or20 are chosen. These values are chosen experimentally.So, in our case, the second fingerprint image is rotated by-10 and its new ROI is found (Figure 12).

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    2.4. Usporeivanje

    ROI-i se dijele na 16 dijelova (Slika 11) te kao i do sada,svaki dio ima svoju dominantnu usmjerenost, ali i sumuudjela ostalih kutova. Neravnopravni udio u slinosti

    imaju ukupno tri varijable. Prva od njih je angle_sum(30% udjela) , angle_equal(50% udjela) iorientation_sum (20% udjela) .

    Slika 11. Podjele ROI-a otiska1 (desno) i otiska2 (lijevo) na 16 dijelova te vektori dominantne usmjerenosti

    Figure 11. Division of fingerprint1 ROI (left) and fingerprint2 ROI (right) into 16 parts and vectors of dominateorientation

    Iznosi za postotke dobiveni su na nain da su se sve trivarijable posebno raunale za otiske koji su identini i posebno za parove koji su razliiti. to je njihovkvocijent vei to odreena mjera vie razlikuje iste otiskeod razliitih te je njezin ukupni udio u slinosti vei.

    Ukoliko je izraunata slinost manja od odreenoga praga dva otiska se ne podudaraju, a ako je vea ili

    ednaka graninoj slinosti tada se na dvije razliite slikenalaze isti otisci prsta.

    2.4. Comparison

    The ROIs are divided into 16 parts (Figure 11), with eachpart having its own dominant orientation and the sum ofother angles. Three variables affect the similarity

    measure. They are: angle_sum (30% share), angle_equal(50% share)and orientation_sum (20% share).

    The specified percentages are obtained in such a way thateach variable has been calculated for the pair of printsthat are identical and the pair that are not. The larger theirquotient, the better the variable discriminates theidentical prints, so it is given a larger percentage value inthe similarity measure.

    If the calculated similarity is smaller than the chosenthreshold, the two fingerprints are not identical.Otherwise, they are identical.

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    Ako primjerice otisak1 i otisak2 imaju 3 kuta od 80,osam od 60 i pet od 20 znai da je postotakS1 100%.Sa svakom razlikom izmeu broja istih kutova dvajuotisaka ovaj postotak pada. Izraeno matematiki:

    (3)

    gdje su n(1) i n(2) brojevi dominantnih kutova dvajuROI-a, P predstavlja broj dijelova (16 dijelova), aveliina k je ukupan broj kutova koje moe poprimiti,odnosno k=180/kut. Ako je kut sliice otiska1 jednakotisku2 tada se S2 poveava za pet. Maksimalnavrijednost koju moe poprimiti je 80 (16 x 5) te u tomsluaju postotakS2 iznosi 100%. Matematiki:

    (4)

    Broj x(N) ovisi o veliini apsolutne razlike izmeu dvakuta. Kree se izmeu 0 (razlika > 40) i 5 (razlika=0).

    Za svaki od 16 podjeljaka oba otiska odreuje se sumaudjela dominantnih kutova te na temelju njihove razlikebroj S3 mijenja svoju veliinu na slian nain kao i S2.Brojy(N) ovisi o veliini razlike dvaju suma, a moe bitiizmeu 0 i 6.

    (5)

    Konaan rezultat usporedbe prikazan je na Slici 12.

    Slika 12. Konaan rezultat usporedbe

    Figure 12. Final result of comparison

    If for example both fingerprints have 3 angles of 80,eight of 60 and five of 20, then the percentage ofangle_sum is 100%. If the number of angles is different,then the percentage drops. Mathematically:

    where n(1) and n(2) represent the number of dominantangles of two ROIs,Prepresents the number of parts (16parts), and kis the total number of angles that can take,that is k=180/angle. If the dominate orientation of the N-th square of both fingerprints is the same, thenangle_equal is increased by 5. The maximum value ofangle_equal is 80 (16 x 5), and the variable is 100%.Mathematically:

    The value x(N) depends on the size of the absolutedifference between two angles. It moves from 0 (difference> 40) to 5 (difference = 0). For each of the 16 squares ofboth fingerprints the sum of the orientations is determinedand on the basis of the difference between the sums offingerprint1 and fingerprint2 the variable orientation_sumchanges its value in a similar way as in (4). The valuey(N)depends on the difference between the two sums, and itvaries from 0 to 6.

    The final result of the comparison is given in Figure 12.

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    Eng. Rev. 28-1 (2008) 39-50 ____________________________________________________________________________________________________________________________________________________

    3. USPOREDBA ROC KRIVULJA

    Rezultati analize veeg broja otisaka iz NIST-4 bazeotisaka prikazani su pomou ROC krivulje (eng. receiveroperating characteristic) na slici 13. NIST-4 je baza odukupno 4000 otisaka (2000 parova) digitaliziranih uJPEG formatu bez gubitaka. Slike su 8-bitne sa 256razina sivila i veliine 512 x 512 piksela.

    Puna krivulja na Slici 13 prikazuje rezultate dobiveneanalizom nasumino odabranih 1000 otisaka. OstaleROC krivulje na Slici 13 su dobivene metodama i filtrimakoritenim u drugim radovima, a prikazane su radiusporedbe. Mjera za ocjenu algoritma za prepoznavanjeotisaka prstiju jest GAR (eng. genuine acceptance rate),odnosno omjer ispravno prihvaenih istih uzoraka iukupnog broja obraenih uzoraka i FAR (eng. false

    acceptance rate), odnosno omjer prihvaenih lanihuzoraka kao istih i ukupnog broja obraenih uzoraka.Njihova meusobna ovisnost predstavlja ROC krivulju.

    Slika 13. Usporedba ROC krivulja

    Figure 13. Comparison of receiver operating characteristic

    4. ZAKLJUAK

    Prezentirani rezultati analize otisaka prstiju suobeavajui, a predloeno je i nekoliko, meusobnozasebnih ideja iskoristivosti ovih filtara. Jednostavnostfan filtara kao i injenica da je odreivanje orijentacijalinija na slici neto to je njima prirodno svojstvo ide uprilog tome. Isto tako i upotreba u praksi, pri automatskoj

    identifikaciji otisaka, ostavlja velik prostor za napredak.

    3. ROC COMPARISON

    The result of the analysis for a larger number offingerprints from the NIST-4 database is given with thereceiver operating characteristic ROC (Figure 13). The

    NIST-4 is a database of 4000 fingerprints (2000 pairs)digitized in JPEG lossless format. Images are 8-bit,greyscale, with a resolution of 512 x 512 pixels.

    The solid line in Figure 13 represents the results obtained by analysing 1000 randomly chosen images offingerprints. The rest of the receiver operatingcharacteristics in Figure 13 are obtained using themethods and filters [6], [7], [8]. The two measures for theevaluation of algorithms for comparing fingerprints arethe genuine acceptance rate GAR (ratio of the numberof correct acceptances and the total number of analyzed pairs) and the false accept rate FAR (ratio of falseacceptances and total number of analyzed pairs). Theirmutual dependence represents the ROC.

    4. CONCLUSION

    The results of the fingerprint analysis presented in this paper are promising, with the simplicity of fan filtersdesign and the fact that determining ridge orientation is ina way an inbuilt feature in them, being the mainadvantages of the proposed method. The application of thefan filter in the automatic identification of fingerprints still,

    however, leaves much room for progress.

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    50 G.Borkovi, M.Vranki, V.Sui: Usmjereni digitalni filtri____________________________________________________________________________________________________________________________________________________

    LITERATURA

    REFERENCES

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    Pattern Recognition, 1999., Volume 2, 187-193[2] M.U.Munir, M.Y.Javed, Fingerprint matching

    using Ridge Patterns, Information andCommunication Technologies, 2005. ICICT 2005.First International Conference on, 27-28 Aug.2005., stranice 116-120

    [3] A.K.Jain, S.Prabhakar, L.Hong, S.Pankanti,Filterbank-based fingerprint matching, ImageProcessing, IEEE Transactions on, Volume 9,Issue 5, May 2000., 846-859

    [4] C.H.Park, J.J.Lee, M.J.T.Smith, S.Park, K.H.Park,Directional Filter Bank-Based Fingerprint FeatureExtraction and Matching, Circuits and Systems forVideo Technology, IEEE Transactions on,Volume 14, Issue 1, Jan. 2004., 74-85

    [5] C.Champod, C.Lennard, P.Margot, M.Stoilovic,Fingerprints and Other Ridge Skin Impressions,CRC Press, Boca Raton (FL), 2004.

    [6] S.Pankanti, S.Prabhakar, A.K.Jain, On theIndividuality of Fingerprints, IEEE Transactionson Pattern Analysis and Machine Intelligence,

    Volume 24, Issue 8, Aug. 2002., I-805 - I-812[7] S.Wang, W.W.Zhang, Y.S.Wang, Fingerprint

    Classification by Directional Fields, Fourth IEEEInternational Conference on MultimodalInterfaces, 2002. Proceedings, 395-399

    [8] D.Maio, D.Maltoni, Direct gray-scale minutiaedetection in fingerprints, Pattern Analysis andMachine Intelligence, IEEE Transactions on,Volume 19, Issue 1, Jan. 1997., 27-40

    [9] M.Tico, P. Kuosmanen, Fingerprint MatchingUsing an Orientation-Based Minutia Descriptor,Pattern Analysis and Machine Intelligence, IEEETransactions on, Volume 25, Issue 8, Aug. 2003.,1009-1014

    [10] X.Jiang, On Orientation and Anistropy Estimationfor Online Fingerprint Autentication, IEEE

    Transactions on Signal Processing, Volume 53,Issue 10, Oct. 2005., 4038-4049[11] H.C.Lee, R.E.Gaensslen, Advances in automted

    fingerprint technology, CRC Press, Boca Raton(FL), 2001.

    [12] C.I.Watson, C.L.Wilson, NIST Special Database 4Fingerprint Database, National Institute ofStandards and Technology, Advanced SystemsDivision, Image Recognition Group, 1992.

    Primljeno / Received: 29.2.2008 Prihvaeno / Accepted: 20.6.2008

    Izvornoznanstveni lanak Original scientific paper

    Adresa autora / Authors addressGoran BorkoviRubei 189a51215 [email protected]. sc. Miroslav Vranki, dipl. ing.Doc. dr. sc. Viktor Sui, dipl. ing.Tehniki fakultet Sveuilita u RijeciVukovarska 5851000 RijekaHRVATSKA

    [email protected]@riteh.hr

    FAR i GAR veliine, to je vidljivo iz ROC krivulje,trenutno nisu konkurentne ostalim rezultatima. S boljimslikama otisaka, odnosno kvalitetnijom predobradom ianalizom slika dobivenih direktno s prsta pomouskenera, umjesto otiskom tinte na papiru (NIST-4), postigli bi se bolji rezultati. Daljnjim istraivanjem teeksperimentiranjem s metodama preciznijeg odreivanjareferentne toke ili tonijeg kompenziranja rotacije,kvaliteta ovoga algoritma bi se unaprijedila.

    The FAR and FRR values, that are available in thereceiver operating characteristic, are currently not asgood as those obtained by applying other comparisonmethods. With better quality of the fingerprintenhancement method, and by acquiring images directlyby live scan, instead of using inked fingerprints (NIST-4), much better results could be obtained. Also, byimproving the accuracy of the reference point selectionand the compensating rotation, the performance of thealgorithm could be further improved.