A MINUTIAE-BASED MATCHING ALGORITHMS IN FINGERPRINT RECOGNITION SYSTEMS 1. INTRODUCTION

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1 JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 13/2009, ISSN Łukasz WIĘCŁAW mnutae ponts, matchng score, fngerprnt matchng A MINUTIAE-BASED MATCHING ALGORITHMS IN FINGERPRINT RECOGNITION SYSTEMS Ths study presents advantages of the most mportant methods of mnutae-based matchng algorthm n fngerprnt recognton systems. Mnuta matchng s the most popular approach to fngerprnt dentfcaton and verfcaton. Fngerprnt matchng usually consst of two procedures: mnuta extracton and mnuta matchng. The performance mostly depends on the accuracy of the mnuta extracton procedure. Mnutae matchng desgnate the tme complexty of appled soluton. 1. INTRODUCTION Fngerprnts are the most used bometrcs technque for personal dentfcaton. There are two man applcatons nvolvng fngerprnts: fngerprnt verfcaton and fngerprnt dentfcaton [1]. Whle the purpose of fngerprnt verfcaton s to verfy the dentty of a person, the goal of fngerprnt dentfcaton s to establsh the dentty of a person. In the past three decades, automatc fngerprnt verfcaton s beng more wdely than other technques of bometrcs such as face dentfcaton and sgnature dentfcaton. Usually assocated wth crmnal dentfcaton, now has become more popular n cvlan applcatons, such as fnancal securty or access control. Many fngerprnt dentfcaton methods have appeared n lterature over the years [1, 5, 7]. The most popular matchng approach for fngerprnt dentfcaton s usually based on lower-level features determned by sngulartes n fnger rdge patterns called mnutae. In general, the two most promnent used features are rdge endng and rdge bfurcaton (Fg. 1). More complex fngerprnt features can be expressed as a combnaton of these two basc features. Mnutae matchng essentally consst of fndng the best algnment between the template (set of mnutae n the database) and a subset of mnutae n the nput fngerprnt, through a geometrc transformaton. a) b) Fg. 1. Example of a) rdge endng and b) bfurcaton. Typcally each detected mnutae where: x, y are coordnates of the mnutae pont, θ t 2. MINUTIAE EXTRACTION m s descrbed by four parameters: m = ( x, y, θ, t ) (1) s mnutae drecton typcally obtaned from local rdge orentaton, s type of the mnutae pont (rdge endng or rdge bfurcaton), The poston of the mnutae pont s at the tp of the rdge or the valley and the drecton s computed to the X axs (Fg 2). Insttute of Informatcs, Unversty of Slesa, Będzńska 39 St., Sosnowec, Poland, lukasz.weclaw@us.edu.pl

2 Fg. 2. Parameters of mnutae a) bfurcaton and b) rdge endng type FEATURE EXTRACTION Two approaches of mnuta extracton process can be found. The smplest and most used method s based on bnarzaton and rdge thnnng stage. Due to a problem of the false mnutae ntroduced by thnnng, some authors proposed drect grey-scale mnutae extracton RIDGE THINNING METHOD The most commonly used method of mnutae extracton s the Crossng Number (CN) concept [2, 3, 4]. The bnary rdge mage needs further processng, before the mnutae features can be extracted. The frst step s to bnarzate and further to thn the rdges, so that they are sngle pxel wde (Fg. 3). A large number of skeletonzaton methods are avalable n the lterature, due to mportant role n many recognton systems. Rata, Chen and Jan [6] adopted a technque ncluded n HIPS lbrary. One of the most tolerant on rregularty of bnary mages s method proposed by Pavlds [7]. Fg. 3. Fngerprnt mage a) bnarzaton and b) skeletonzaton. The mnutae ponts are determned by scannng the local neghbourhood of each pxel n the rdge thnned mage, usng a 3 3 wndow (Fg. 4). 66 Fg. 4. a) Rdge endng and b) bfurcaton n c) 3 3 wndow.

3 pxels The CN value s then computed, whch s defned as half the sum of the dfferences between pars of neghbourng p and p + 1 [8]: 8 1 CN = p p, p = p (2) ( x, y) = 1 Usng the propertes of the CN as shown n Table (Fg. 5), the rdge pxel can be then classfed as a rdge endng, bfurcaton or non-mnutae pont. CN Property 0 Isolated pont 1 Rdge endng 2 Contnung rdge 3 Bfurcaton 4 Crossng Fg. 5. Propertes of the Crossng Number. The man problem, n the mnutae extracton method usng rdge thnnng processes, comes from the fact that mnutae n the skeleton mage do not always correspond wth true mnutae n the fngerprnt mage. In fact, a lot of false mnutae are extracted because of undesred spkes, breaks, and holes. For ths reason, tme-consumng enhancement algorthms are requred pror to thnnng stage [9] DIRECT GREY-SCALE METHOD Mnutae extracton approaches, that work drectly on the grey-scale mages, wthout bnarzaton and thnnng, was nduced by these consderaton [9,10]: enhancement algorthms are tme-consumng, a sgnfcant amount of nformaton may be lost durng the bnarzaton process, skeletonzaton may ntroduce a large number of false mnutae unsatsfactory results when appled to low qualty mages. Mao and Malton [10] proposed a drect-grey scale mnutae extracton technque. Ther basc dea s rdge tracng, by salng accordng to the local orentaton. The rdge lne algorthm attempts to locate at each step, the local maxma, relatve to a secton perpendcular to the local rdge drecton. The algorthm avods revstng the same rdge, by keepng track of the ponts traced so far. They also compared ther method to bnarzaton and thnnng approaches and concluded that rdge followng, sgnfcantly reduce computaton tme. Nlsson and Bgun [11] proposed usng Lnear Symmetry (LS) flter n the mnutae extract approach, based on the concept that mnutae are local dscontnutes of the LS vector feld. Two types of symmetres - parabolc symmetry and lnear symmetry are adapted to model and locate the ponts n the grey-scale mage, where there s lack of symmetry (Fg. 6). a) b) Fg. 6. Symmetry flter response n the mnutae pont. a) rdge bfurcaton, b) rdge endng (from [12]). Fnally, Govndaraju, Schneder and Sh [13] proposed a new algorthm based on chan code contour followng. Chan codes have been used n computer vson to descrbe the shapes of object boundares and n ths case they are loss-less representaton of rdge contours, at the same tme yeldng a wde range of nformaton about the contour such as curvature, drecton, length etc [13]. As the contour of the rdges s traced consstently n a counter-clockwse drecton, the mnutae ponts are encountered as locatons, where the contour has a sgnfcant turn. Specfcally, the rdge end occurs as sgnfcant left turn and the bfurcaton as a sgnfcant rght turn n the contour (Fg. 7). Analytcally, the turnng drecton may be determned by consderng the sgn of the cross product of the ncomng and outgong vectors at each pont. 67

4 a) b) Fg. 7. a) Mnutae marked by sgnfcant turn n the contour, b) the contour extracted by tracng the rdge boundares n a counter clockwse drecton ORIENTATION ESTIMATION Fngerprnt mages can be consdered as an orented texture pattern. The orentaton feld of a fngerprnt mage prescrbes the local orentaton of the rdges, contaned n the fngerprnt. Therefore orentaton feld defne the drecton of the mnutae. There have been several approaches to estmate the orentaton feld of a fngerprnt mage. Approaches based on pxel algnment relatve to a fxed number of reference orentaton [14, 15] do not provde very accurate estmates (Fg 8b). Fg. 8. a) Fngerprnt mage, b) dscrete orentaton feld, c) orentaton feld estmated by last mean square method. The smplest and most natural approach for orentaton feld estmaton s based on computaton of gradents n the fngerprnt mage. The least mean square estmaton method employed by Hong [16] s most popular. The local orentaton at pxel (, j ) can then be estmated usng the followng equatons: where: (, j) W W + j + ( ) ( ) (3) V (, j) = 2 u, v u, v, x x y W W u = v= j W W + j + ( ) ( ) (4) V (, j) = u, v u, v, y x y W W u= v= j 1 V 1 y (, j ) ( j) θ, = tan, (5) 2 V, x ( j) θ - s the least square estmate of the local orentaton at the block centred at pxel (, j ), x, y - are the gradent magntudes (the Sobel operator) n the x and y drectons. Further, orentaton feld need to be smoothed n a local neghbourhood usng a Gaussan flter. 68

5 3. MATCHING SCORE In a good qualty rolled fngerprnt mage, there are about 70 to 80 mnutae ponts and n a latent fngerprnt the number of mnutae s much less (approxmately 20 to 30) [5]. A mnutae-based fngerprnt matchng system usually returns the number of matched mnutae on both query and reference fngerprnts and uses t to generate smlarty scores. Accordng to forensc gudelnes, when two fngerprnts have a mnmum of 12 matched mnutae, they are consdered to have come from the same fnger [3]. Matchng algorthm compares two mnutae sets: template T { m1, m2,..., m j} = { } from the query and returns smlarty score (, ) I m1, m2,..., m The mnutae par then tolerance dstances: m and S T I. = from reference fngerprnt and nput m j are consdered to be match only f dfference n ther poston and drectons are lower sd ( m, m j ) 1 ( x x j ) ( y y j ) r0 (, j ) 1 mn ( j,360 j ) 0 = + (6) dd m m = θ θ θ θ < θ (7) In addton, t should be noted, that n some cases the bfurcaton and rdge endng ponts can be dffcult to dstngush between each other. Hence, n practce, most fngerprnt dentfcaton systems do not make a dfference between bfurcatons and rdge endngs, when matchng mnutae ponts [11]. 4. MATCHING ALGORITHMS For matchng regular szed fngerprnt mages, a brute-force matchng, whch examnes all the possble solutons, s not feasble snce the number of possble solutons ncreases exponentally wth the number of feature ponts on the prnts [3]. Transformaton of nput mnutae set, s the most mportant step, n order to maxmze the value of smlarty score. Let map be transformaton functon that maps the mnuta set from I to I accordng to gven geometrcal transformaton. Then, matchng problem can be formulated as: n S ( T, I ) = max md ( m, mapm ( m ')) m (8) = 1 (, j ) (, j ) (, j ) md m m = sd m m dd m m (9) where: n - s the number of mnutae ponts n I nput set, m - s the number of transformaton equal to the number of mnutae n T template set, Fg. 9. Mnutae based matchng. Ratha [17] proposed a method that searches the geometrc transformaton parameters n four-dmensonal Hough space. By specfyng scale, rotaton and shft parameters, a Hough transform was conducted on a mnutae set. A score can be obtaned by specfyng these three parameters. 69

6 Jan [18] proposed an algnment-based matchng method, n whch adopted the assocated rdge to algn the nput mnutae wth the template mnutae. Good performance s reported to overcome deformaton. However, Tong notced [19], that f only short part of a rdges s saved, the algorthm may results n naccurate algnment. Furthermore, sometmes t s dffcult to fnd long rdges n a thnned fngerprnt mage. Jang and Yau [20] proposed a mnuta matchng method, usng both global and local structure of features. In ths technque, local structure was used to fnd the correspondence par of mnutae and the global structure was used to compute smlarty score. However, f less neghbourhood mnutae s used, false reject rate may arse n case of the presence of false mnutae. Tco and Kuosmanen [21] adopted an Feature-based mnuta descrptor for mnutae matchng, and good performance are reported. However, rdge count are not ncluded n the descrptor, whch has been wdely used and reported good performance [20,22]. Lee [23] proposed a local algnment method. In ths method, rdge frequency value was used to mnmze dstance error, by normalzng the dstance between mnutae. But the mnmzng dstance by frequency makes the algorthm more tmeconsumng. 5. CONCLUSIONS In ths paper, a mnuta matchng systems has been descrbed. A mnutae-based fngerprnt verfcaton system s dvded n two man blocks: the feature extracton block and the matchng block. Man problem n feature extracton secton s qualty of fngerprnt mage. Low qualty areas of fngerprnt occurs large number of false mnutae pont. Most mportant n matchng stage, s selecton of tolerance dstances and transformaton method. When tolerance values are ncreasng, then false accept rate s also rsng. When transformaton of nput mnutae set s not precse, then false reject rate value s hgh. BIBLIOGRAPHY 70 [1] BEBIS G., DEACONU T., GEORGIOPOULOS M., Fngerprnt Identfcaton Usng Delaunay Trangulaton, Proc. of Int. Conf. on Informaton Intellgence and Systems, pp , Washngton, DC, USA, [2] AMENGUAL J., JUAN A., PREZ J., PRAT F., SEZ S., VILAR J., Real-tme mnutae extracton n fngerprnt mages, Proc. of the 6th Int. Conf. on Image Processng and ts Applcatons, pp , Ireland, [3] MEHTRE B. M., Fngerprnt mage analyss for automatc dentfcaton, Machne Vson and Applcatons 6, 2, pp , Inda, [4] BOASHASH B., DERICHE M., KASAEI S., Fngerprnt feature extracton usng block-drecton on reconstructed mages, IEEE regon TEN Conf., dgtal sgnal Processng applcatons, TENCON pp , Australa, [5] GOVINDARAJU V., JEA T., Mnutae-based partal fngerprnt recognton, Pattern Recognton, Vol. 38, pp , USA, [6] CHEN S., JAIN A., RATHA K., Adaptve Flow Orentaton-Based Feature Extracton n. Fngerprnt Images, Pattern Recognton, Vol. 28, No. 11, pp , USA, [7] PAVLIDIS T., A thnnng algorthm for dscrete bnary mages. Computer Graphcs and Image Processng, Vol. 13, pp , [8] TAMURA H., A comparson of lne thnnng algorthms from dgtal geometry vewpont. Proc. of the 4th Int. Conf. on Pattern Recognton, pp , [9] MALTONI D., MAIO D., JAIN A.K., PRABHAKAR S., Handbook of Fngerprnt Recognton. Sprnger, New York, [10] MAIO D., MALTONI D., Drect Gray-Scale Mnutae Detecton In Fngerprnts, IEEE Trans. Pattern Anal. Machne. Intell., vol 19, pp , USA, [11] BIGUN J., NILSSON K., Usng lnear symmetry features as a pre-processng step for fngerprnt mages, Conf. Audo and Vdeo Based Bometrc Person Authentcaton, pp , Sweden, [12] BIGUN J., HARTWIG FRONTHALER K., Local feature extracton n fngerprnts by complex flterng, Advances n Bometrc Person Authentcaton, LNCS, Vol. 3781, pp.77 84, [13] GOVINDARAJU V., SCHNEIDER J., SHI Z., Feature Extracton Usng a Chancoded Contour Representaton, Int. Conf. on Audo and Vdeo Based Bometrc Person Authentcaton, UK, [14] KAWAGOE M., TOJO A., Fngerprnt pattern classfcaton, Pattern Recognton, Vol 17, No. 3, pp , USA, 1987 [15] PORWIK P., Fast fngerprnt recognton method based on reference pont locaton, Proc. of the IEEE Workshop on Sgnal Processng. Poznań, 29 September, pp , [16] HONG L., JAIN A., WAN Y., Fngerprnt mage enhancement: algorthm and performance evaluaton, IEEE Trans. Pattern Analyss and Machne Intellgence, Vol.20, No.8, pp , [17] CHEN S., JAIN A., KARU K., RATHA K., A real-tme matchng system for large fngerprnt databases, IEEE Trans. Pattern Anal. Machne. Intell, Vol. 18, pp , USA, [18] BOLLE R., HONG L., JAIN A., On-lne fgnerprnt verfcaton, IEEE Trans. Pattern Anal. Machne. Intell, Vol. 19, No. 4, pp , USA, 1997.

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