Sensors 203, 3, 5048-5067; do:0.3390/s35048 Artcle OPEN ACCESS sensors ISSN 424-8220 www.mdp.com/journal/sensors Fnger-Ven Verfcaton Based on Mult-Features Fuson Huafeng Qn,2, *, Lan Qn 2, Lan Xue 2, Xpng He, Chengbo Yu 3 and Xnyuan Lang 2 3 School of Computer Scence and Informaton Engneerng, Chongqng Technology and Busness Unversty, Chongqng 400030, Chna; E-Mals: jsjhxp@ctbu.edu.cn (X.H.); lxy3400@sna.com (X.L.) Key Laboratory of Optoelectronc Technology and Systems of Mnstry of Educaton, College of Opto-Electronc Engneerng, Chongqng Unversty, Chongqng 400030, Chna; E-Mals: qnlan@cqu.edu.cn (L.Q.); xuelan@cqu.edu.cn (L.X.) College of Electronc and Automaton, Chongqng Unversty of Technology, Chongqng 400045, Chna; E-Mal: yuchengbo@cqut.edu.cn * Author to whom correspondence should be addressed; E-Mal: qnhuafengfeng@63.com; Tel.: +86-38-8375-8680. Receved: 6 July 203; n revsed form: 4 October 203 / Accepted: 2 October 203 / Publshed: 5 November 203 Abstract: Ths paper presents a new scheme to mprove the performance of fnger-ven dentfcaton systems. Frstly, a ven pattern extracton method to extract the fnger-ven shape and orentaton features s proposed. Secondly, to accommodate the potental local and global varatons at the same tme, a regon-based matchng scheme s nvestgated by employng the Scale Invarant Feature Transform (SIFT) matchng method. Fnally, the fnger-ven shape, orentaton and SIFT features are combned to further enhance the performance. The expermental results on databases of 426 and 70 fngers demonstrate the consstent superorty of the proposed approach. Keywords: personal dentfcaton; fnger-ven; scale nvarant feature transform; orentaton encodng; mult-features fuson. Introducton Wth the growng demand for more user frendly and strngent securty, automatc personal dentfcaton has become one of the most crtcal and challengng tasks. Thus, some researchers are
Sensors 203, 3 5049 motvated to explore new bometrc features and trats. The physcal and behavoral characterstcs of people,.e., bometrcs, have been wdely employed by law enforcement agences to dentfy crmnals. Compared to tradtonal dentfcaton technques such as cards, passwords, the bometrc technques based on human physologcal trats can ensure hgher securty and more convenence for the user. Therefore, the bometrcs-based automated human dentfcaton are now becomng more and more popular n a wde range of cvlan applcatons. Currently, a number of bometrc characterstcs have been employed to acheve the dentfcaton task and can be broadly categorzed n two categores: () extrnsc bometrc features,.e., faces, fngerprnts, palm-prnts and rs scans; (2) ntrnsc bometrc features,.e., fnger-vens, hand-vens and palm-vens. The extrnsc bometrc features are easy to spoof because ther fake versons can be successfully employed to mpersonate the dentfcaton. In addton, the advantages of easy accessblty of these extrnsc bometrc trats also generate some concerns on prvacy and securty. On the contrast, the ntrnsc bometrc features do not reman on the capturng devce when the user nteracts wth the bometrcs devce, whch ensures hgh securty n cvlan applcatons. However, there are lmtatons n palm-ven and hand ven verfcaton systems due to the larger capture devces requred. Fortunately, the sze of fnger-ven capture devces can be made much smaller so that t can be easly embedded n varous applcaton devces. Moreover, usng the fnger for dentfcaton s more convenent for the users. In ths context, personal authentcaton usng fnger-ven features has receved a lot of research nterest [ 7]. Currently, many methods are developed to extract ven patterns from the captured mages wth rregular shadng and nose. Mura [5] et al. proposed a repeated lne trackng algorthm to extract fnger-ven patterns. Ther expermental results show that ther method can mprove the performance of the ven dentfcaton. Subsequently, to robustly extract the precse detals of the depcted vens, they nvestgated a maxmum curvature pont method [6]. The robustness n the extracton of fnger-ven patterns can be sgnfcantly mproved based on calculatng local maxmum curvatures n cross-sectonal profles of a ven mage. Zhang [7] et al. have successfully nvestgated fnger-ven dentfcaton based on curvelets and local nterconnecton structure neural networks. The Radon transform s ntroduced to extract ven patterns and the neural network technque s employed for classfcaton n reference [8]. The performance usng ths approach s better than that of other methods. Lee and Park [9] have recently nvestgated fnger-ven mage restoraton methods to deal wth skn scatterng and optcal blurrng usng pont spread functons. Expermental results suggest that the performance of fnger-ven recognton usng restored fnger mages can be mproved sgnfcantly. In our prevous work [0], an effectve method based on mnutae feature matchng was proposed for fnger-ven recognton. To further mprove performance, a regon growth-based feature extracton method [] s employed to extract the ven patterns from unclear mages. For a small database, the two methods can acheve hgh accuracy by matchng these mages. Currently, a wde lne detector s beng nvestgated for fnger-ven feature extracton by Huang et al. [2]. Ther expermental results have shown that a wde lne detector combned wth pattern normalzaton can obtan the best results among these methods. Meanwhle, a new fnger-ven extracton method usng the mean curvature [3] s developed to extract the pattern from the mages wth unclear vens. As the mean curvature s a functon of the locaton and does not depend on the drecton, t acheves better performance than other methods.
Sensors 203, 3 5050 The ven feature extracton methods descrbed above have shown better performance for fnger-ven recognton, however, they have the followng lmtatons: () as some of the pattern extracton methods such as maxmum curvature [6] and mean curvatures [3] emphasze the pxel curvature, the nose and rregular shadng are easly enhanced. Thus, they cannot detect effectve ven patterns for authentcaton; (2) The methods descrbed above only focus on sngle feature extracton (the shape of vens), rather than mult-feature extracton. However, t s dffcult to extract a robust ven pattern because the captured ven mages contan rregular shadng and nose, therefore, only by usng the shape of ven patterns one cannot acheve robust performance n fnger-ven recognton; (3) The matchng scores generated from these methods are ether global or local, so t s dffcult to accommodate the local and global changes at the same tme. To solve these problems, a new scheme s proposed heren for fnger-ven recognton. The man contrbutons from ths paper can be summarzed as follows: Frstly, ths paper proposes a new approach whch can extract two dfferent types of fnger-ven features and acheves a most promsng performance. Unlke the exstng approaches based on curvature [6,3], the proposed method emphaszes the dfference value of the two curvatures n any two orthogonal tangental drectons, so the fnger regon ven can be dstngushed from other regons such as the flat regon, the solated nose and rregular shadng. Meanwhle, the fnger-ven orentaton s also estmated by computng the maxmum dfference value. Secondly, we proposed a localzed matchng method to accommodate the potental local and global varatons at same tme. The localzed ven sub-regons are obtaned accordng to feature ponts whch can be determned by the mproved feature ponts removal scheme n the SIFT framework. Then the matchng scores are generated by matchng the correspondng parttons n two mages. Thrdly, ths paper nvestgates an approach for fnger-ven dentfcaton combnng SIFT features, shape and orentaton of fnger-vens. As dfferent knds of features reflect objects n dfferent aspects, the combnaton strategy should be more robust and mprove performance. The expermental results suggest the superorty of the proposed scheme. Fgure. Block dagram for personal dentfcaton usng fnger-ven mages. The rest of ths paper s organzed as follows: Secton 2 detals our proposed feature extracton method. Secton 3 descrbes the matchng approach for the fnger-ven verfcaton. In Secton 4, we
Sensors 203, 3 505 obtan combnaton scores based on two fuson approaches and the expermental results and dscusson are presented n Secton 5. Fnally, the key conclusons from ths paper are summarzed n Secton 6. 2. Fnger Image Shape Feature Extracton and Orentaton Estmaton The block dagram of the proposed system s shown n Fgure. In ths secton, we wll extract the fnger-ven shape and orentaton patterns based on the dfference curvature. 2.. The Extracton of Fnger-Ven Shape Feature The curvature has been successfully appled n mage segmentaton, edge detecton, and mage enhancement. Mura et al. [6] and Song et al. [3] brought ths concept nto fnger-ven segmentaton, and ther expermental results have shown that the method based on curvature can acheve mpressve performance. However, the two methods based on the curvature only emphasze the curvature of pxel, so the nose and rregular shadng n a fnger-ven mage are easly enhanced. To further extract effectve ven patterns, we proposed a new fnger-ven extracton method based on curvature of pxel dfference, whch s shown as follows. Suppose that F s a fnger-ven mage, and Fxy (, ) s the gray value of pxel (, x y ). A cross-sectonal profle of pont ( x, y ) n any drecton s denoted by P(z). Its curvature s computed as follows: '' P() z 2 3/2 Kz ()= {+ Pz '( ) } '' 2 2 where P() z=d P/ dz and P'(z)=d P/ dz. Therefore, the maxmum dfference curvature can be defned as: D max = max K 0 () (2) K () z K + / 2() z f /2 where K (0 ), and K ( z) and /2 K () z K / 2() z f /2 K () z represent the curvatures n the drecton and the drecton perpendcular to, respectvely. The enhancement ven mage s obtaned after computng maxmum dfference curvature of all pxels. Then the ven pattern s bnarzed usng a threshold. It s worthwhle to hghlght several aspects of the proposed method here:. For the ven regons, the curvature s large n rdge drecton and small n the drecton perpendcular to the rdge drecton. Therefore, D max s large. 2. For the flat regons, the curvatures n all drectons are small, so the maxmum dfferences D max are small. 3. For the solated nose and rregular shadng, the curvature n all drectons s large, but D max s stll small. Accordng to the analyss above, the ven regon can be dstngushed from other regons effectvely, so the dfference curvature method can obtan robust ven patterns.
Sensors 203, 3 5052 2.2. Orentaton Estmaton The orentaton encoded method s appled to palm-prnts [8] and palm-vens [9] and has shown hgh performance. Therefore, we attempt to preserve the orentaton features of fnger-ven by the followng encodng method: [Step] Determnaton of Orentaton As the fnger-ven extends along the fnger, the fnger-ven has a clear orentaton feld. To estmate the orentaton, we dvde the rdge drecton of a pxel ( x, y ) nto eght drectons, whch s shown n Fgure 2. Then the eght drectons are dvded nto four groups and the two drectons n each group are perpendcular to each other. Let G {, j j 4} be jth group. j Fgure 2. Eght drectons of a pxel. 3 4 5 6 7 2 8 (x,y) 8 2 7 6 5 4 3 [Step2] Computaton of Dfference Curvature: The curvatures n two drectons G j can be computed usng Equaton (), respectvely. The dfference values of the curvatures n each group are calculated as: K j z K j 4 z f j 4 Kj ( j,2,, 8) (3) K j z K j 4 z f j 4 [Step 3] Encodng The Orentaton of Ven Based on Equaton (3), the rdge orentaton of pxel ( x, y ) s determned as follows: j =arg{ max ( K )} max j j {,2,, 8} (4) It should be ponted out that the largest rotatonal changes ths orentaton encodng scheme can address s / 8, snce the drectons of all pxels are quantzed to only eght orentatons. If the number of quantzed orentatons s too small, the encodng scheme s robust to the rotaton varatons, and not dstngushable. On the contrast, t s senstve to the rotaton varatons and the genune match. Therefore, the performance of the encodng scheme depends on number of orentatons. In our work, the number of quantzed orentatons s set to 8.
Sensors 203, 3 5053 3. Sub-Regon Matchng Method Currently, there are two major ven matchng methods: the mnuta-based matchng method [0,4,5] and the shape-based matchng method [5,6,,3]. These matchng methods usng mnuta can acheve hgh accuracy for hgh resoluton ven mages. Unfortunately, the mnuta ponts are easly dsturbed and lost for low resoluton ven mages, whch can sgnfcantly reduce the performance. Therefore, most researchers employ the ven shape to match two mages, and develop some matchng methods [5,6] to address translaton varatons n both horzontal and vertcal drectons. However, these matchng methods are senstve to the potental deformaton because the matchng scores generated from them are the global matchng scores. In most cases, the matchng scores among the localzed ven regons (n two mages) are more robust to local/partal dstortons. Therefore, some researchers [9] use the sub-regon matchng method to overcome the local deformaton. Unfortunately, as the sub-regon matchng method parttons an mage nto dfferent non-overlappng blocks, t can not overcome the global varatons such as whole translaton or rotaton between two mages. Therefore, exstng ven matchng methods [3 4,6,7,9] cannot accommodate the local changes (local deformaton) and global varatons (global translaton, and global rotaton) at the same tme. To overcome ths drawback, we propose a new localzed matchng method based on the SIFT feature. Frstly, an mproved SIFT match method s employed to determne the correct matchng ponts. Accordng to these matchng ponts, a fnger mage s parttoned nto many nonoverlappng or overlappng localzed sub-regons. Then the match scores are obtaned by matchng the correspondng parttons n two mages. Fnally, we combne the matchng scores of each patch. 3.. Determnaton of Feature Ponts Based on the Improved Sft Matchng Method SIFT s a very powerful local descrptor, whch s nvarant to mage scale, translaton and rotaton, and s shown to provde robust matchng across a substantal range of affne dstorton, addton of nose, and changes n llumnaton. Recently, the local descrptor was employed for palm-ven recognton [20] and hand ven recognton [2]. SIFT descrpton ncludes four steps [22]: () Scale-space extremum detecton; (2) Feature ponts localzaton; (3) Orentaton assgnment; (4) Feature ponts descrptor. After processng by above steps, each mage s descrbed by a set of the 28-elements SIFT nvarant features. Let P={ p },, 2 m, j, 2 n are the feature pont sets of gallery and probe mage respectvely, where m and Q { q j } and n are the number of SIFT feature. The matchng method proposed by Lowe [22] s as follows. The Eucldean dstances between a gallery feature pont and all the SIFT features n the probe mage are computed by: d p q d * (, ) mn j j,2,, n (,2 m) (5) * where dj d( p, qj ) denotes the Eucldean dstance between two SIFT descrptors p and q j. q s the closest neghbor pont of p. However, many features from a gallery mage wll not have any correct matches n the probe mage because some feature ponts were not detected n the probe mage. To enhance matchng performance,
Sensors 203, 3 5054 these msmatchng feature ponts are dscarded by comparng the dstance of the closest neghbor to that of the second-closest neghbor: where ' q s the second-closest neghbor pont of ' * * ' d d( p, q ) f d( p, q ) cd( p, q) (6) p, and c s a constant. Ths method s an effectve way to remove msmatchng feature ponts detected from the mage where there are sharp changes between dfferent regons of an mage. However, fnger-ven mages are non-rgd, round and smooth objects and contan few straght edges. The ntensty changes n fner ven mages are gradual and slow, so the blob and corner structures are not sgnfcantly dfferent from ther neghborng pxels. Therefore, t s dffcult to remove msmatchng feature ponts n the fnger-ven mages usng Equaton (6), whch can degrade the matchng accuracy. To solve ths problem, we use followng method to remove the msmatchng ponts. * Based on Equaton (5), we can obtan m SIFT dstances d( p, q ), 2,, m. Let ( x, y ) and * * ( x, y ) be the spatal postons of a gallery feature pont p and a probe feature pont q *. The * * geometrc dstance g( p, q ) between p and q s computed as: g( p, q ) ( x x ) ( y y ) (7) * * 2 * 2 * The m geometrc dstances g( p, q ) are arranged n order of ncreasng number and form a new ' ' array { g( p, )} m q. The feature ponts correspondng to the k smallest geometrc dstances of ' ' { g( p, )} m q are denoted by ' ' ( p, ) k q, where the k s determned as: T f T m k (8) m else where m s the total number of the feature ponts whch s usually dfferent for each par of dfferent mages, and T s threshold whch s set to 20 expermentally. Therefore, k pars of feature ponts ' ' ( p, ) k q are remaned as the matchng ponts. On the contrast, other m-k feature ponts are dscarded as the msmatchng ponts. Fgure 3b shows 20 pars of matchng feature ponts obtaned from Fgure 3a by our removal scheme. 3.2. Generatng Sft Score and Sub-Regon from Gallery and Probe Images The k pars of matchng feature ponts are generated from gallery and probe mages (orgnal grayscale mages) n the prevous secton. Based on these feature ponts, the SIFT dstance between a gallery and probe mage s computed by: k ' ' sft d( p, q ) (9) k In addton, the ven shape or orentaton mage can be dvded nto k nonoverlappng or overlappng sub-regons usng the spatal postons of k feature ponts. Let A and B represent the template generated from the gallery and probe fnger-ven mages (ven shape or orentaton), respectvely. Then the k sub-regons from A and B are denoted by:
Sensors 203, 3 5055 ' wa, ha wa, ha A a a A k,,2, } (0) ' wb, hb wb, hb B b b B k,,2, } () a, a where w h b, b a and b w h denote the localzed sub-regons separated from A and B, w a and h a are wa, ha wb, hb wdth and heght of sub-regon a, and w b and h b are wdth and heght of sub-regon b ( wa wb, and h a h b ). The center pont of each sub-regon s the matchng feature ponts. Fgure 3c shows the relatonshp between sub-regon and feature pont n a fnger-ven shape mage. Fgure 3. Matchng results of SIFT features for fnger-ven mages from a same person. (a) Feature ponts obtaned by orgnal SIFT method; (b) Feature ponts obtaned by mproved method; (c) The sub-regons for two pars of matchng ponts. (a) (b) 3.3. Generatng the Matchng Scores of Fnger-Ven Shape Images (c) ' ' Suppose that the sub-regons A and B are generated from the gallery and probe fnger-ven shape mages A and B, respectvely. The matchng scores between A and B are computed as: k ' ' S( A, B) S( A, B ) (2) where s the matchng scores between two correspondng sub-regons, whch s computed as follows: k wb hb wa, ha wb, hb ( a ( u x, v y), b ( u, v)) u 0 v 0 = mn ( ) x [0, wa wb], y [0, ha hb] wb hb (3) x and y are the amount of translaton n horzontal and vertcal drectons, respectvely. Let P and P 2 be the values of the pxels located n the gallery and probe mages, respectvely. s defned as follows:
Sensors 203, 3 5056 f P P 2 ( P, P2)= 0 otherwse (4) In ths approach, the partton scheme based on SIFT descrptor s nvarable to global translaton, and global rotaton, and the matchng n Equaton (3) addresses the possble local varatons by matchng the two sub regons correspondngly wth a small amount of shftng. Therefore, the SIFT based matchng scheme s expected to accommodate possble local and global varatons. 3.4. Generatng the Matchng Scores of Fnger-Ven Orentaton In Secton 2, the fnger-ven orentaton feature s extracted by the proposed encodng approach. The partton scheme and localzed matchng method of the orentaton encoded template s smlar to these of fnger-ven shape template. Therefore, the matchng scores between two encoded fnger-ven templates can be generated usng Equaton (2), except for the replacement functon wth followng formulaton: f P P 2 0 ( P, P2)= 0 otherwse (5) In our paper, the szes of localzed sub-regons generated from the gallery and probe ven shape templates are 20 60 and 0 50, respectvely. The szes of localzed sub-regons for two fnger-ven orentaton templates are 60 40 and 50 30. 4. Generatng Fuson Matchng Scores The exstng fnger-ven recognton methods [3 3,6,7] cannot employ the ven orentaton pattern but rather use the ven shape pattern for dentfcaton. However, the ven pattern may not be effectvely extracted due to the condtons of a sensor, the health condtons of humans, llumnaton varatons and so on. Therefore, a sngle ven pattern cannot work well for the fnger-ven recognton task. To resolve ths problem, the combnaton strategy ntegratng fnger-ven features, fnger orentaton encodng and SIFT feature s proposed to enhance the performance. Accordng to the stage at whch the fuson takes place, fuson s performed at three dfferent processng levels such as feature, score, and decson level [23]. Score level fuson s commonly appled n bometrc systems because the matchng scores reman suffcent dstngushable nformaton for dentfcaton. Therefore, after obtanng the fnger-ven shape and orentaton encodng scores by Equaton (2) and the SIFT matchng score based on Equaton (9), we combne three matchng scores at score level based on two commonly used combnaton strateges (weghted sum and support vector classfcaton, SVM). To mprove performance, these scores are normalzed by the z-score normalzaton scheme [24], whch s defned as: ' z u z (6) where z s the matchng scores from fner-ven shape, orentaton and SIFT features, and u and are the arthmetc mean and the standard devaton of z. Then the normalzed scores z ' are used as the nput of combnaton strateges.
Sensors 203, 3 5057 4.. Weghted Sum Rule-Based Fuson The weghted combnaton strategy has been hghly successful appled n bometrcs [25,26]. Ths approach can acheve separaton of the genune and mposter scores by searchng for the lnear combnaton and s represented as follows. Let { z ' ' ', z2,, z n } be normalzed scores from a fnger, where n s the number of classfer: where n n ' (7) zf z w w = and zf s the combned matchng score. z ' denotes the score from the th classfer and w represents correspondng weght. In our experments, the optmal weghts for the matchng scores were emprcally determned. 4.2. Support Vector Machnes (Svm)-Based Fuson Currently, SVM has been appled to the classfcaton task n multmodal bometrc authentcaton, such as n [27], and [28] and has shown promsng performance. Ths approach can separate the tranng data nto two classes wth a hyperplane that maxmzes the margn between them [29,30]. In our experments, the optmal decson hyperplanes of the SVMs were determned by a radal bass ' functon (RBF) kernel. Let ( z c ) j,2, m be the tranng data, where z ' ( z ', z ',, z ' ) s a j j j j 2j nj score vector wth n classfers and c j s the correspondng class label. j vector sample and for an mposter score vector. After performng the tranng, a weghted matrx s preserved for classfcaton. At the testng phase, a testng score vector z can be classfed as a genune class or an mpostor class. The SVM tranng was acheved wth C-support vector classfcaton (C-SVC) n the SVM tool developed by Chang and Ln [3]. 5. Expermental Results 5.. Database c s set to for a genune score In order to test the performance of the proposed schemes, we performed rgorous experments on our database and another fnger-ven database provded by the Natonal Tawan Unversty of Scence and Technology [32]. All mages n the two databases are fltered pror to dentfcaton experments usng a two dmensonal Gaussan flter wth sze 5 5 pxels and standard devaton 3 pxels. As the dstance between the fnger and the camera s fxed, the captured mages n each database have the same sze. Then the proposed method and some prevous methods [6,3,33] are employed to enhance the ven mages. For far comparson, the enhancement ven mage s bnarzed usng a global threshold. We mplement these approaches usng MATLAB 7.9 on a desktop wth 2 GB of RAM and Intel Core 5-240M CPU. Fgure 4 llustrates the output of varous methods.
Sensors 203, 3 5058 Fgure 4. Sample results from dfferent feature extracton methods: (a) Fnger-ven mages from two databases (Top left mage from database A and bottom left mage from database B); (b) Ven pattern from maxmum curvature; (c) Ven pattern from mean curvature; (d) Even Gabor wth Morphologcal; (e) Ven pattern from dfference curvature; and (f) Orentaton pattern from dfference curvature. Database A (a) (b) (c) (d) (e) (f) For our database, all the mages were taken aganst a dark homogeneous background and subject to varatons such as translatons, rotatons and local/partal dstortons. Ths database comprses 4,260 dfferent mages of 7 dstnct volunteers. Each volunteer provded three fnger mages (ndex fnger, mddle fnger and rng fnger) from the left and rght hands respectvely, and each fnger has 0 dfferent mage samples. Therefore, each volunteer provded 60 mages wth a sze of 352 288. The black background s removed by croppng the orgnal mages and the sze of the remanng mage s 22 83 pxels. Database B The fnger-ven database was bult at the Informaton Securty and Parallel Processng Laboratory Natonal Tawan Unversty of Scence and Technology and conssts of two parts: Images and Matchng Scores. The Matchng Scores folder conssts of 2 50 genune scores and 2 57,20 mpostor scores generated from left and rght-ndex fngers, respectvely. The Images folder contans 680 grayscale mages of 85 ndvduals, each subject havng four dfferent mages from left and rght-ndex fnger respectvely. Therefore, there are eght mage samples for each person. The sze of
Sensors 203, 3 5059 each mage s 320 240 pxels. These orgnal mages contan a black background, whch should degrade the matchng accuracy. Thus we crop them to a dmenson of 20 90. 5.2. Expermental Results on Database A The objectve n ths experment s to evaluate the robustness of the proposed method for a relatvely larger fnger-ven dataset. Frstly, matchng s performed on the ndex, mddle and rng fnger mages ndvdually, and the matchng sets are defned as follows: () genune scores set: matchng the ten samples from same fngers to each other, resultng n 6,390 (42 45) genune scores; (2) mposter scores set: the ten fnger-ven samples from same fnger are randomly parttoned nto two subsamples (fve for tranng and the reman for testng). Thus 25 (5 5) mposter scores are produced from two dfferent fngers. Then we nterchanged ther roles and performed the evaluaton once more. The average results across two trals are reported, and the number of mposter scores s 250,275 (42 4 25/2). Secondly, the dfferent fnger mages (ndex, mddle and rng fnger mages) from the same subjects were treated as dfferent classes, thus the total number of classes s 426. Smlar to above steps, there are 9,70 (426 45) genune scores and 2,263,25 (426 425 25/2) mposter scores. Fnally, the performance of varous feature extracton approaches s evaluated by the equal error rate (EER). The EER s the error rate when the false acceptance rate (FAR) and false rejecton rate (FRR) values are equal. The FAR value s the error rate of falsely acceptng mpostor and the FRR value s the error rate of falsely rejectng genune. The recever operatng characterstc (ROC) can be obtaned by combned FAR and genune accept rate (GAR) (GAR = FRR). We compared the performance of our method wth the other methods such as SIFT, maxmum curvature, mean curvature and even Gabor wth morphologcal. Table lsts the EER of the varous methods, and the ROC for the correspondng performances s llustrated n Fgure 5. Table. Performance from fnger-ven matchng wth varous approaches (Database A). Approaches Index Fnger (%) Mddle Fnger (%) Rng Fnger (%) Index, Mddle and Rng Fnger (%) Maxmum curvature 9.43 5.4 0.34 0.9 Mean curvature 7.92 8.0 6.35 7.44 SIFT feature 4.5 6.48 7.39 6.09 Even Gabor wth Morphologcal 6.78 5.45 5.5 5.79 Dfference curvature d (orentaton) 7.08 7.69 7.78 7.59 Dfference curvature (ven) 3.34 3.36 3.26 3.32 It can be ascertaned from Table and Fgure 5 that usng the dfference curvature method to extract ven pattern acheves the best performance among all the approaches referenced n ths work. The success results are contrbuted by emphaszng on the dfferences among curvatures nstead of the magntude. Therefore, the solated nose and rregular shadng can be removed from the extracted mages. The maxmum curvature approach and mean curvature approach do not acheve robust performance n our ven database. The poor performance (Table and Fgure 5) can be attrbuted to
Sensors 203, 3 5060 ths fact that the two methods emphasze the curvatures of pxels, so the nose and rregular shadng are also emphaszed. Therefore, the extracton ven mage contans many noses (as shown n Fgure 4), whch can degrade the performance of fnger-ven recognton system. Even Gabor wth morphologcal emphaszes on the shape/structure features of the ven. After the ven patterns are processed by the morphologcal approach, all the ven lnes/curves are processed to smlar wdth. However, the wdth of the ven lnes s actually dfferent from the palm to the fnger tp. Therefore, the performance of even Gabor wth morphologcal approach s lmted for ven pattern extracton. Fgure 5. Recever operatng characterstcs from fnger-ven mages (Database A). (a) Index-fnger (b) Mddle-fnger (c) Rng-fnger (d) Index-fnger, mddle-fnger and rng-fnger mages. Recever Operatng Characterstc(Index-Fnger) Recever Operatng Characterstc(Mddle-Fnger) 0.9 0.9 0.8 0.8 0.7 0.7 GAR 0.6 0.5 0.4 0.3 0.2 0. Maxmum curvature Mean curvature SIFT feature Even Gabor wth Morphologcal Dfference Curvature(orentaton) Dfference Curvature(ven) 0-4 0-3 0-2 0-0 0 FAR (a) Recever Operatng Characterstc(Rng-Fnger) GAR 0.6 0.5 0.4 0.3 0.2 0. Maxmum curvature Mean curvature SIFT feature Even Gabor wth Morphologcal Dfference Curvature(orentaton) Dfference Curvature(ven) 0-4 0-3 0-2 0-0 0 FAR (b) Recever Operatng Characterstc 0.9 0.9 GAR 0.8 0.7 0.6 GAR 0.8 0.7 0.6 0.5 0.5 0.4 0.3 0.2 Maxmum curvature Mean curvature SIFT feature Even Gabor wth Morphologcal Dfference Curvature(orentaton) Dfference Curvature(ven) 0-4 0-3 0-2 0-0 0 FAR (c) 0.4 0.3 0.2 0. Maxmum curvature Mean curvature SIFT feature Even Gabor wth Morphologcal Dfference Curvature(orentaton) Dfference Curvature(ven) 0-4 0-3 0-2 0-0 0 FAR (d) In our experment, the SIFT feature dose not work well on our fnger-ven system. Ths can be explaned that the fnger-ven mage contans less local feature ponts because t s a non-rgd, round and smooth object. In addton, the performance acheved by the ven orentaton s smlar to that of the mean curvature method [3], whch mples that the orentaton of vens also contans mportant dscrmnatng power. From the expermental results, some performances for the rng fnger are better than those for ndex and mddle fnger, but t does not mply than the rng fnger contans more ven
Sensors 203, 3 506 patterns. There s no evdence to show that the performance for one fnger should be better than that of another. The dfferent performance for three fngers may be caused by the behavor of the users. 5.3. Expermental Results on Database B To further ascertan the robustness of our method, the expermental results on the database B are reported n ths secton. The performance was frstly evaluated on the left and rght-ndex fngers, respectvely. Therefore, the number of genune score and mposter scores are 50 (85 6) and 57,20 (85 84 6/2) respectvely. Smlar to experments A, there are n total 70 classes when dfferent fngers from the same persons that were regarded as belongng to dfferent classes. One thousand twenty (70 6) genune scores and 229,840 (70 69 6/2) mposter scores are thus generated from the same fngers and dfferent fngers. In addton, reference [32] has also shown the genune scores and mposter scores generated from ther database (referred to Secton 5. database B), whch s computed by the state of art method [3], so the EER and ROC of ther approach can be drectly obtaned based on these matchng scores. Therefore, we compare not only these approaches n prevous experments but also the approach n [32] wth the proposed method n ths experment. The EER of varous methods have been summarzed n Table 2. Fgure 6 has llustrated the ROC for the correspondng performances. Table 2. Performance from fnger-ven matchng wth varous approaches (Database B). Approaches Left-Index Fnger (%) Rght-Index Fnger (%) Left and Rght-Index Fnger (%) Scores n [32] 2.55.57 2.6 Maxmum curvature 6.68 7.84 7.36 Mean curvature.76 2.6 2.06 SIFT feature.88 9.6 0.98 Even Gabor wth Morphologcal.56 3.4.76 Dfference curvature (orentaton) 4.90 4.6 4.7 Dfference curvature (ven) 0.98.0.08 The expermental results summarzed n Table 2 (and Fgure 6) are qute smlar to the trends from experments n prevous secton (experments A). The proposed method acheves the lowest EER for left-ndex fnger, rght-ndex fnger and n combnaton, respectvely. Fgure 6 shows that the proposed method can acheve more than 90% GAR at the lower FAR, whch s hgher than that of other methods. The expermental results on the databases A and B consstently show that the proposed ven extracton method outperforms other methods n the verfcaton scenaro. However, the expermental results on database B n ths secton are comparatvely better than those n prevous secton generated from database A. Ths can be explaned by the resultng smaller wthn-class varatons, whch could be possbly attrbuted to less fnger-ven mages from same fnger (.e., only four dfferent mages from a fnger) n database B.
Sensors 203, 3 5062 Fgure 6. Recever operatng characterstcs from fnger-ven mages (Database B). (a) Left ndex-fnger (b) Rght ndex-fngers (c) Left and rght ndex-fngers. Recever Operatng Characterstc(Left-Index Fnger) Recever Operatng Characterstc(Rght-ndex Fnger) 0.9 0.9 GAR 0.8 0.7 0.6 0.5 Scores n [30] Maxmum curvature Mean curvature SIFT feature Even Gabor wth Morphologcal Dfference Curvature(orentaton) Dfference Curvature(ven) FAR (a) 0 0 GAR 0.8 0.7 0.6 0.5 Recever Operatng Characterstc(Left and Rght-Index Fngers) Scores n [30] Maxmum curvature Mean curvature SIFT feature Even Gabor wth Morphologcal Dfference Curvature(orentaton) Dfference Curvature(ven) (b) FAR 0 0 0.9 0.8 GAR 0.7 0.6 0.5 0.4 Scores n [30] Maxmum curvature Mean curvature SIFT feature Even Gabor wth Morphologcal Dfference Curvature(orentaton) Dfference Curvature(ven) 0-4 0-2 0 0 FAR (c) 5.4. Performance of Sub-Regon Matchng Method The expermental results presented n ths secton are to estmate the performance of the SIFT-based sub-regon matchng method. For our approach, the gallery and probe fnger-ven mages are parttoned nto dfferent sub-regons based on feature ponts and then the matchng scores between two correspondng sub-regons are computed usng Equaton (3). Based on the matchng score of each sub-regon, the matchng score between gallery and probe mages can be computed by Equaton (2). For the global matchng approach, the gallery and probe fnger-ven mage are used as a whole and submtted nto Equaton (3), and then the global matchng score s obtaned. We compare two matchng schemes based on fnger-ven shape mages from two databases (ndex, mddle and rng-fnger for database A, and Left and rght-ndex fnger for database B). The performance from the two databases usng two dfferent matchng schemes s shown n Fgure 7. It can be observed that the proposed sub-regon matchng approach has acheved better performance than global matchng method. Ths superor performance can be attrbuted to the fact that the sub-regon matchng scheme s more robustness to the local and global varatons. In addton, the proposed approach parttons the
Sensors 203, 3 5063 templates nto dfferent sub-regons and thus has ncreased the tranng samples to some extent as compared to global matchng scheme. Fgure 7. Recever operatng characterstcs from two databases for fnger-ven shape mages wth dfferent matchng approaches. 0.95 0.9 0.85 GAR 0.8 0.75 0.7 0.65 Proposed approach on database A Global matchng approach on database A Proposed approach on database B Global matchng approach on database B 0-4 0-3 0-2 0-0 0 FAR 5.5. Performance from SIFT Feature, Fnger-Ven Shape and Orentaton Encoded Combnaton In ths secton, the expermental results are presented to test the performance mprovement that can be acheved by combnng SIFT feature, fnger-ven shape and orentaton features based on weghted SUM and SVM fuson rules. For each experment, half of mposter and genune scores are randomly selected for tranng and remanng scores are used for testng. Ths parttonng of the scores was repeated 20 tmes, and then we compute the mean of GAR (at certan FAR) and EER on the 20 testng sets to evaluate the performance of fnger-ven recognton system. These parameters of z-score normalzaton were estmated based on tranng data and three knds of normalzed scores are used as the nput of two fuson rules. For the weghted SUM fuson rule, the weghts are selected expermentally. For the SVM-based fuson rule, the hghest classfcaton accuracy among varous kernels such as dot, neural, radal, polynomal and analyss of varance kernels was obtaned by a radal-based kernel. The parameters g (gamma n the RBF kernel functon) and c (cost of C-SVC functon [3]) are set to 0.006 and.2 expermentally. The weghts of SUM fuson rule and SVM are lsted n Table 3. The expermental results on the database A and database B are summarzed n Tables 4 and 5, respectvely. For database A, the combned performance acheved n Table 4 s better than those n Table. Comparng the results n Tables 2 and 5, there are consstent trends. The expermental results on two databases suggest that the combnaton of smultaneously acqured fnger-ven, fnger orentaton and SIFT feature can mprove the performance. The combned performance on database B s hgher than those on database A, whch s attrbuted to the better performance n Table 2 over Table (as dscussed n prevous secton).
Sensors 203, 3 5064 Table 3. Weghts of SUM fuson rule and SVM for two databases. Fuson Method weghted SUM fuson rule SVM fuson rule Database A Database B Database A Database B Weghts Database SIFT Feature Ven Shape Orentaton Encoded Index-fnger 0.5 0.7 0.5 Mddle-fnger 0. 0.65 0.25 Rng-fnger 0.5 0.7 0.5 Index, Mddle and Rng-fnger 0. 0.7 0.2 Left-ndex fnger 0. 0.7 0.2 Rght-ndex fnger 0.5 0.75 0. Left and rght-ndex fnger 0. 0.8 0. Index-fnger 6.5255 85.3080 42.578 Mddle-fnger 6.778 78.3649 4.8224 Rng-fnger 5.30 85.5667 45.9337 Index, Mddle and Rng-fnger 48.544 98.896 55.8290 Left-ndex fnger 6.0424 52.2029 7.9522 Rght-ndex fnger 5.9473 33.6064 24.848 Left and rght-ndex fnger 2.555 55.8496 5.42 Table 4. Performance from the combnaton of SIFT, shape, and orentaton of ven (database A). Fuson Method Data GAR(%) FAR(%) EER(%) weghted SUM fuson rule SVM fuson rule Index-fnger 86.42 0.0099 2.70 Mddle-fnger 86.6 0.004 2.77 Rng-fnger 86.48 0.003 2.74 Index, Mddle and Rng-fnger 86.56 0.003 2.74 Index-fnger 86.53 0.004 2.63 Mddle-fnger 86.20 0.003 2.70 Rng-fnger 87.5 0.003 2.67 Index, Mddle and Rng-fnger 86.67 0.0099 2.68 Table 5. Performance from the combnaton of SIFT, shape, and orentaton of ven (database B). Fuson Method Data GAR(%) FAR(%) EER(%) weghted SUM fuson rule SVM fuson rule Left-ndex fnger 94.45 0.00 0.96 Rght-ndex fnger 93.73 0.005 0.97 Left and rght-ndex fnger 95.0 0.0096 0.79 Left-ndex fnger 94.63 0.005 0.96 Rght-ndex fnger 94.08 0.00 0.89 Left and rght-ndex fnger 95.04 0.0096 0.78
Sensors 203, 3 5065 6. Conclusons In ths paper, we have nvestgated a novel fnger-ven dentfcaton scheme utlzng the SIFT, fnger-ven shape and orentaton features. Frstly, two feature extracton approaches are proposed to obtan fnger-ven shape and orentaton features. Then we proposed a sub-regon matchng method to overcome the local and global changes between two ven mages. Fnally, a combnaton scheme s employed to mprove the performance of fnger-ven recognton system. Rgorous expermental results on two dfferent databases have shown that the proposed ven extracton method outperforms prevous approaches and a sgnfcant mprovement n the performance can be acheved by combnng SIFT features, fnger-ven shape and orentaton features. Acknowledgments Ths work was fnancally supported by the scence and technology talents tranng plan Project of Chongqng (Grant No. cstc203kjrc-qnrc4003), the Scentfc Research Foundaton of Chongqng Technology and Busness Unversty(Grant No.203-56-04), the Natural Scence Foundaton Project of CQ (Grant No. CSTC 200 BB2259) and the Scence Technology Project of Chongqng Educaton Commttee (Grant No. KJ2078). The authors thankfully acknowledge the Natonal Tawan Unversty of Scence and Technology for provdng the fnger-ven database used n ths work. Conflct of Interest The authors declare no conflct of nterest. References. Kono, M.; Uek, H.; Umemura, S. Near-nfrared fnger ven patterns for personal dentfcaton. Appl. Opt. 2002, 4, 7429 7436. 2. Hashmoto, J. Fnger Ven Authentcaton Technology and ts Future. In Proceedngs of the 2006 Symposa on VLSI Technology and Crcuts, Honolulu, HI, USA, 3 7 June 2006; pp. 5 8. 3. Mulyono, D.; Jnn, H.S. A Study of Fnger Ven Bometrc for Personal Identfcaton. In Proceedngs of the Internatonal Symposum on Bometrcs and Securty Technologes (ISBAST 2008), Islamabad, Pakstan, 23 24 Aprl 2008; pp. 6. 4. L, S.Z.; Jan, A. Encyclopeda of Bometrcs; Sprnger: Pttsburgh, PA, USA, 2009. 5. Mura, N.; Nagasaka, A.; Myatake, T. Feature extracton of fnger-ven patterns based on repeated lne trackng and ts applcaton to personal dentfcaton. Mach. Vs. Appl. 2004, 5, 94 203. 6. Mura, N.; Nagasaka, A.; Myatake, T. Extracton of Fnger-Ven Patterns Usng Maxmum Curvature Ponts n Image Profles. In Proceedngs of the IAPR Conference on Machne Vson Applcatons, Tsukuba Scence Cty, Japan, 6 8 May 2005; pp. 347 350. 7. Zhang, Z.; Ma, S.; Han, X. Multscale Feature Extracton of Fnger-Ven Patterns based on Curvelets and Local Interconnecton Structure Neural Network. In Proceedngs of the 8th Internatonal Conference on Pattern Recognton, Hong Kong, 20 24 August 2006.
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