A Novel ngerprnt Matchng Method Combnng Geometrc and Texture eatures Me Xe, Chengpu Yu and Jn Q Unversty of Electronc Scence and Technology of Chna. Chengdu,P.R.Chna xeme@ee.uestc.edu.cn Post Code:6154 Abstract: In ths paper, we proposed a new fngerprnt matchng algorthm based on local geometrc feature of fngerprnt mnuta and texture feature for each mnuta. To descrbe the geometrc feature of fngerprnt mnuta, we buld a b-mnuta based bar model and get the geometrc relatonshp between the bar and rdges of two canddate mnuta; to demonstrate the texture feature, we creatvely adopt gradent angular hstogram n the neghborhood regon of mnuta. Meanwhle, changeable szed boundary box of unque area adopted for mnuta matchng make ths algorthm more robust to nonlnear fngerprnt deformaton. nally, expermental results on the database VC24 demonstrate that our method s effectve and relable, whlst the matchng accuracy can be mproved to some extent after usng gradent angular hstogram as texture feature wthout addng extra amount of calculaton. 1 Introducton Automatc fngerprnt recognton, whch s establshed n modern nformaton technology, s wldly used to cvlan purposes such as access control, fnancal securty, and so on. ngerprnt recognton technology s based on the realty that fngerprnt of each person have ts unqueness and unchangeable propertes. Many researchers have made progress n the fngerprnt matchng algorthms. These algorthms can manly be dvded nto several categores as follows: (1) mnuta and rdge based method [15]; (2) mnuta and texture based method [14]; (3) graph based method [12] [13]; (4) tr-mnuta structure based method [7]. All the conspcuous features used n above methods are related and complemented. Takng nto account of advantages of above methods, we propose the method based on b-mnuta based bar model, and add texture feature to fngerprnt representaton to mprove the matchng effcency and accurateness. The algorthm ntroduced n ths paper adopts local geometrc feature of m-
156 Me Xe, Chengpu Yu and Jn Q nuta and texture feature around mnuta. B-mnuta based bar model used n ths method can compromse the defcency of solo-mnuta based and tr-mnuta based model, and t can effectvely and relably extract matched mnuta pars. Meanwhle, the gradent angular hstogram as texture feature can generally reflect the profle of fngerprnt rdge edges and the mnuta type (bfurcaton or endng). Ths paper s organzed as follows. Secton 2 brefly descrbes preprocessng of fngerprnt mage. Secton 3 demonstrates local structure and reference mnuta selecton. Secton 4 llustrates fngerprnt algnment and global matchng. Secton 5 shows the expermental results on the database VC24. In the end, we draw the concluson n secton 6. 2 Preprocessng of fngerprnt mage In ths paper, we adopt fngerprnt features not only from thnned rdge mage, such as mnuta poston and geometrc feature, but also from orgnal gray mage, such as gradent angular hstogram as texture feature. Snce low qualty fngerprnt mage often contans noses and contamnaton, t requres us to preprocess the mage to enhance mage. Man steps nvolved n the preprocessng nclude fngerprnt segmentaton, block orentaton estmaton, mage enhancement, mage bnarzaton, thnnng, and mnuta extracton. LnHong [1] ntroduces the orentaton estmaton method based on gradent vectors of fngerprnt rdges, whch could compute drectons more accurately n low qualty mage, see gure 1(b). Zhu [2] proposes Gabor flterng enhancement method can overcome the defcency that occurs when usng method n LHong[1], see gure 1(c). X.P.Luo[3] descrbes bnarzaton and post processng of fngerprnt mage wth method based on knowledge and method based on combnaton of statstc and structure, see gure 1(d). g. 1 ngerprnt preprocessng.(a)an orgnal fngerprnt n DB1_Aof VC24,(b)ts block orentaton feld,(c)enhanced mage,(d) thnned mage of fngerprnt mage 3 Local structure descrpton and reference mnuta selecton Mnuta features (endng or bfurcaton) are salent and stable features for fn-
A Novel ngerprnt Matchng Method Combnng Geometrc and Texture eatures 157 gerprnt mage of dfferent dscrmnaton. However, external nterference wll cause many pseudo mnutas and create large error, and usng mult-mnuta based model can resst nterference to some extent. 3.1 eature descrpton of solo-mnuta based model and calculaton of gradent angular hstogram Vector set of mnuta M { M ( x,, h 1 h 8 ); M 1 } denote all comprehensve mnuta n the fngerprnt. Where M s the number of mnuta n a fngerprnt mage. M, the th mnuta, s denoted by a feature vector x,, h 1 h 8. Where (a) x denotes ts coordnates; (b) denotes the tangent drecton of the rdge where the mnuta locates; (c) h 1 h 8 denotes gradent angular hstogram n the crcular neghborhood regon whose radus s r. The algorthm of gradent vector computaton [5] has been descrbed n the secton of fngerprnt orentaton feld estmaton. Then we transform the vector n Eucldean space to the polar coordnates, thus the magntude and drecton can be calculated as follows: 2 2 1 / 2 f mag f ) [ G x G ] (6) ( y G y arctan( ) (7) G mag( ) and f n formula (6) represent magntude calculaton operator and magntude value respectvely. n formula (7) represents drecton of gradent vector, whch s perpendcular to the drecton of the correspondng mage edge. Gradent angular hstogram s consdered as texture feature for fngerprnt recognton. Utlzaton of gradent angular hstogram n ths paper has much preponderance whch are showed as follows: 1) t s more stable to the change of llumnaton, because t extracts the gradent angles of larger gradent magntude here; 2) t s nvarant to scale and dsplacement of the mage; 3) t s nvarant to rotaton. Here, we defne the tangent angle of the rdge where mnuta locates as the reference drecton. x g. 2 Gradent angular hstograms of two matched mnuta pars from the same fngerprnt llustrated as stem graphs. (1) Upper stem graphs represent gradent angular hstogram of endngs, smlarty between them s.929; (2) Lower stem graphs llustrate that of bfurcatons, smlarty between them s.975.
158 Me Xe, Chengpu Yu and Jn Q Gradent angular hstograms of two matched mnuta pars have been demonstrated n gure 2. In the stem graph, drecton 1 and drecton 8 are neghborhood. rom the gradent angular hstogram, we can observe that all the gradent angles concentrate n the regon whose center s the drecton of rdge where mnuta locates. Gradent angular hstogram adopted here can reflect the nformaton of mnuta type to some extent, and t also can reflect the texture nformaton around the mnuta. 3.2 eature descrpton of b-mnuta based model Vector set of b-mnuta based model E { E ( p, q, l, c,, u, v ), E 1} represents nformaton of all b-mnuta bars, and these features are showed n gure 3. (a). (b). E s the sze of b-mnuta bar set. p, q denote the seral numbers n the mnuta set are two endng mnuta of b-mnuta bar lsted n the mnuta set. ( x p p p q q E M. M p and M q, and they contan all features (c). l ( x ) ( x ) denotes the length of b-mnuta bar, and p ),( x q q ) represent coordnates of respectvely. In order to lmt the number of b-mnuta bars n a fngerprnt mage, the length of b-mnuta bars are confned n range L l l L h (d). c denote the number of fngerprnt rdges that bar E passes through, because there wll exst large error f we only calculate Eucldean dstance; however, combnng c to Eucldean dstance can accurately descrbe correspondng dstance. x p x (e). q arctan denotes the drecton of b-mnuta bar. y p y q M p and M (f). u mn{, }, v mn{, } denote p p p p b-mnuta bar s drectonal devatons from and, and ths feature have rotaton nvarant ablty.. p q q q q q q g. 3 Demonstraton of mnuta structure and features n a rdge mage
A Novel ngerprnt Matchng Method Combnng Geometrc and Texture eatures 159 Influenced by the low qualty n a fngerprnt, mnuta extracted mght not be so stable, hence we haven t added mnuta type as a character n b-mnuta based model [11]. Here, the angular hstogram around the mnuta as the texture can express the type of mnuta. 3.3 Selecton of reference mnuta pars for fngerprnt matchng As fngerprnt mage are extracted n the same devce, subtle deformaton of the fngerprnt can be omtted. Solo-mnuta based model [6] used n fngerprnt matchng wll generate lots of pseudo matched mnuta pars; tr-mnuta based model [7] used n fngerprnt matchng mght have dffculty n lookng for entrely matched trangle composed of three mnuta, so that we adopt b-mnuta bar based model as the compromse of two above models. G Suppose b-mnuta bar sets E and E denote fngerprnt representaton of nput and template fngerprnts, and matchng crtera ncludes: G (a). Constrant of bar-length: l l d, where d usually makes value about half the rdge perod n the fngerprnt; G (b). Constrant of rdge numbers the bar passes through: c c n, where n usually makes value about 1 or 2; (c). Constrant of drectonal devaton of rdges and b-mnuta bar: G G u u u and v v v, where u and v usually make values lower than /12. G G (d). Constrant of texture smlarty: s ( M p, M p ) s, s ( M q, M ) q s, where s should be larger than.9, whch s obtaned from numerous experment. The smlarty of two vector s ( h, h ) can be estmated as correlaton coeffcent: L h, h h ( k ) h ( k ) k 1 s ( h, h ) (8) L L h h 2 2 h ( k ) h ( k ) k 1 k 1 Where denotes nner producton operator and represents norm operator for a vector. (e). Determnaton of two matched mnuta pars from two matched bars: G G G G p p, q q f u u u and v v v (9) G G G G p q, q p f u v u and v u v Mnuta pars satsfyng all above condtons may be selected as canddate reference mnuta pars. Through rgd constrants of features derved from local geometrc and texture nformaton, matched reference mnuta pars could dramatcally decrease.
16 Me Xe, Chengpu Yu and Jn Q 4 ngerprnt algnment and global matchng Jan[9] demonstrates mnuta matchng method utlzng polar coordnate system whch s scale and rotaton nvarant. Suppose mnuta M n nput fngerprnt and mnuta M G n template fngerprnt are matched reference mnuta, and algnment of nput fngerprnt and template fngerprnt s carred out wth locomoton and rotaton of nput fngerprnt. Rotate angle of nput fngerprnt s r G r r, and the coordnate vector of reference mnuta s ( x ). Then, mnuta coordnate ( x ) n Eucldean space can be transformed n to polar coordnate vector as follows: r e r 2 r ( x x ) ( y y ) r y y arctan r x x r e Where ( r, ) s the fnal polar coordnate vector. In order to search matched mnuta pars effectvely and to guarantee robust to fngerprnt deformaton, we propose a changeable szed boundary box of unque area n ths paper, because unque area of boundary box allows consstent error tolerance to each mnuta. In Eucldean coordnate system, unque area of boundary box can easly be obtaned; however, n polar coordnate system, parameters of a boundary box can be determned as follows: 2 (1) g. 4 Demonstraton of changeable szed boundary box of unque area S In gure 4, area of boundary box s fxed n the experment, and radus of boundary box r s also fxed n the experment, then the polar angle of boundary box wll decrease when polar radus of the correspondng mnuta ncreases. The polar angle of boundary box can be calculated as follows: S /( r * R ) (11) Where, all parameters n formula (11) are showed n gure 5. In order to determne whether two fngerprnts are from the same source, we should calculate smlarty of all possble mnuta pars n two fngerprnts after algnment and compute global smlarty of two fngerprnts. Condtons that should be satsfed for two matched fngerprnts are lsted as follows: G a) r / 2 b) G / 2 G G c) u u u d) s ( M, M ) s
A Novel ngerprnt Matchng Method Combnng Geometrc and Texture eatures 161 Where, (a) and (b) are used to test whether two mnuta are n the same boundary box or not; (c) s used to test whether two tangent drectons of correspondng rdges are consstent; (d) s used to test texture smlarty of two mnuta regons. Statstc all matched mnuta pars between nput and template fngerprnts whch s marked as N match, and global smlarty of two fngerprnt can be estmated as follows: N match rmatch (12) mn{ N, N } Where, rmatch n formula (12) s regarded as mnuta matchng rate; N nput s consdered as number of vald mnuta n nput fngerprnt; N template s deemed as number of vald mnuta n template fngerprnt. Eventually, we could udge two fngerprnts are matched f r match s larger than a fxed threshold, and a optmal threshold should be determned n experments n order to get global optmzaton to all performance ndcators whch wll be dscussed n secton 5. nput template 5 Expermental results 5.1 Advantages of gradent angular hstogram as texture feature In ths experment, we choose fngerprnt mages 1_3.gf and 1_6.gf representng average qualty fngerprnt and low qualty fngerprnt respectvely from sub-database DB1_A n VC 24. Durng the process of searchng matched b-mnuta bars n ths experment, parameters nvolved are defned as follows: (1) Eucldean length of b-mnuta bar s confned n range between 5 and 15 tmes of rdge perod, namely [1~8]; (2) Error constrant of b-mnuta bar s Eucldean length d 4 ; (3) Error constrant of rdges that b-mnuta bar passes through n 1; (4) Drectonal devaton constrant between rdge and b-mnuta bar of certan mnuta u v / 12 ; (5) Texture smlarty constrant of gradent angular hstogram s. 9 ; Table 1. Comparson of canddate reference mnuta pars ngerprnt Mnuta numbers Matched bars B-mnuta bars(wthout consderng gradent angular hstogram) 1_3.gf 62 421 565 57 Matched bars after utlzng gradent angular hstogram 1_6.gf 37 221 565 57 rom TABLE 1, we can observe that canddate reference b-mnuta bars decrease from 565 to 57 after consderng constrant of texture smlarty of gradent
162 Me Xe, Chengpu Yu and Jn Q angular hstogram, so that the amount of calculaton of subsequent global matchng wll reduce about 9-1 tmes. Eventually, the rato of retaned canddate reference b-mnuta bars to all nvolved bars s.6%. 57 421 221 6 matched pars 5 4 3 2 1.5.6.7.8.9 1 texture smlarty g. 5. Curve n the graph represents the number of reference mnuta pars wth smlarty of gradent angular hstogram of mages 1_3.gf and 1_6.gf n DB1_A of VC24. rom gure 5, we can observe that matched canddate b-mnuta bars wll decrease dramatcally when texture smlarty as constrant becomes hgher. However, the threshold of texture smlarty shouldn t be too hgh from numerous expermental observatons. If the threshold of texture smlarty s too hgh, t wll omt many genune and vtal b-mnuta bars; nversely, matched b-mnuta bars wll ncrease sgnfcantly. After selectng optmal canddate reference b-mnuta bar, parameters used to statstc global matched mnuta are defned as follows: (1) Radus error of boundary box r 1 ; (2) Polar angular error of boundary box S /( r R ) 1 / R ; (3) Tangent drectonal error of two correspondng rdges u /12 ; (4) Threshold of texture smlarty constrant s. 9. 1 3 4 2 5 7 6 11 13 12 9 8 17 1 16 18 14 15 19 1 2 3 4 5 6 7 8 9 1 11 12 13 14 17 16 18 15 19 g. 6 Matched mnuta pars n two mages of the same fngerprnt; the red lnes n the pcture denotes optmal reference b-mnuta bar; the blue numbers n the pcture represent correspondng orders of matched mnuta pars. The left s the rdge mage of 1_3.gf n DB1_A of VC24, whle the rght s 1_6.gf. rom gure 6, we can observe that the algorthm proposed n ths paper can accurately fnd reference b-mnuta bar and obtan matched mnuta pars of the whole fngerprnt. In ths case, t has detected 19 pars of matched mnuta, and smaller mnuta set has 37 vald mnuta ponts, thus fnal mnuta matchng rate attan 5%.
A Novel ngerprnt Matchng Method Combnng Geometrc and Texture eatures 163 5.2 Performance of our method on VC24 Nowadays, performance ndcators of fngerprnt matchng that are wldly accepted nclude NMR, MR, EER, NMR1, NMR1 and ZeroMR. All above ndcators can be reflected from the ROC curve, whose horzontal axs denotes MR and vertcal axs denotes MNR. Every subset n VC24 contans 1 fngerprnts, and each fngerprnt has 8 samples, thus t has 8 fngerprnt mages n a subset. Expermental data n estmatng NMR has ((8*7) /2) * 1 = 2,8 pars; and total Expermental data n estmatng MR has ((1*99) /2) = 4,95 pars when only utlzng the frst sample for each fngerprnt. g. 7 ROC curves of the experment results for database VC24. (a) Red curve llustrates the experment results of DB1_A of VC24, whlst blue demonstrates DB2_A. (b) Blue curve represents the experment result of b-mnuta bar model wthout texture smlarty for DB1_A n VC24, whlst red represents the experment result of our method for DB1_A. rom gure 7(b), we can observe that ROC curve of our method n ths paper llustrates better performance than that of method only consderng b-mnuta bar model. Moreover, EER s about 1% observed from EER lne, whch s much lower than method only utlzng b-mnuta bar model whose EER s 15%. Table 2. Results of Our New Method over the Two Databases among VC24 Database EER MR1 MR1 ZeroMR (VC24) (%) (%) (%) (%) DB1_A 9.56 17.2 23. 37.2 DB2_A 7.46 2. 25.4 42. Observng from TABLE 2, expermental results of the algorthm n ths paper are actually close wth that of many excellent algorthms [1],[11], whch demonstrates robustness and relance of our method n ths paper. Of course, the expermental results wll become much better f we have a good performance of fngerprnt preprocessng, such as segmentaton, enhancement and bnarzaton. Especally, many genune mnuta ponts wll be omtted and pseudo mnuta ponts wll be forged f parameters of Gabor flter are not optmal.
164 Me Xe, Chengpu Yu and Jn Q 6 Conclusons In ths paper, we ntroduce a novel algorthm of fngerprnt matchng based on combnaton of mnuta geometrc and texture features. We adopt gradent angular hstogram as texture feature n ths paper, whch effectvely represents fngerprnt nformaton where mnuta locate, because t can generally reflect profle of rdge edges and mnuta types to some extent. In addton, we adopt b-mnuta bar model [1] as the geometrc feature n ths paper. The new texture feature of gradent angular hstogram n ths paper can guarantee the accuracy of mnuta matchng of fngerprnt; nonetheless, the gradent angular hstogram wll create devaton for the reason of low qualty fngerprnt. In the process of global fngerprnt matchng, usng rato of matched mnuta pars to total mnuta can largely measure the smlarty of two fngerprnts. However, the matchng accurateness mght be mproved f we could measure smlartes from dverse aspects of correspondng weghts [8]. 7 References [1] Ln Hong, ngeprnt Image Enhancement: Algorthm and Performance Evaluaton, IEEE Trans. Pattern Analyss and Machne Intellgence, Vol. 2, No. 8, August 1998. [2] Zhu En, Automatc fngerprnt recognton technology, Publshng House of Natonal Unversty of Technology of Securty, May 26:95-17. [3] Xpng Luo, Knowledge Based ngerprnt Image Enhancement, 15th ICPR, Vol.4, P783-786. [4] Je Tan, Technology of Bometrc eature Recognton and ts Applcaton, Publshng House of Electronc Industry, September 25: 85-97. [5] Rafael C. Gonzalez, Dgtal Image Processng (Second Edton), Publshng House of Electroncs Industry, July 25:567-585. [6] Xpng Luo, A mnuta matchng algorthm n fngerprnt verfcaton, 15th ICPR, Vol.4, pp.833~836, Barcelona, 2. [7] Xudong Jang, ngerprnt mnutae matchng based on the local and global structures, IEEE,2:142~145. [8] MaoL Wen, Integraton of multple fngerprnt matchng algorthms, Proceedngs of the fth Internatonal Conference on Machne Learnng and Cybernetcs, Dalan, 13-16 August 26. [9] A.K.Jan,LnHong,On-lne dentty authentcaton system usng fngerprnts, Proceedngs of IEEE, 1997,85:1365~1388. [1] Yulang He,Je Tan, ngerprnt Matchng Based on Global Comprehensve Smlty, IEEE Trans. Pattern Analyss and Machne Intellgence, Vol. 28, No. 6, August 26. [11] Zhu En, Automatc fngerprnt recognton technology, Publshng House of Natonal Unversty of Technology of Securty, May 26:138-154. [12] D.Isenor, S.Zaky. ngerprnt dentfcaton usng graph matchng. Pattern Recognton, 1986,19:113~122 [13] XaJan Chen,Je Tan. A matchng algorthm based on local topologc structure. Proceedngs of ICIAR24, LNCS3211,24:36~367. [14] Jan A.K.,Hong L..lterbank-based ngerprnt matchng. IEEE Transactons on Image Processng, 2, 19(5):846~859. [15] Aparecdo Nlceu Marana. Rdge-Based ngerprnt Matchng Usng Hough Transform. Proceedngs of the XVIII Brazlan SIBGRAPI 5:153~1834.