Int. J. Bometrcs, Vol., No., 28 47 Dynamc sgnature recognton based on velocty changes of some features Rafał Doroz, Potr Porwk*, Tomasz Para and Krzysztof Wróbel Insttute of Informatcs, Unversty of Slesa Będzńska 39, 4-2 Sosnowec, Poland E-mal: doroz@zsk.tech.us.edu.pl E-mal: porwk@us.edu.pl E-mal: para@us.edu.pl E-mal: kwrobel@zsk.tech.us.edu.pl *Correspondng author Abstract: Dynamc sgnature analyss allows us to regster ndvduals and ther hdden human behavour. Ths paper presents a stroke-based approach to dynamc analyss of sgnature. Indvdual features can be dentfed by fndng the dscrete sgnature ponts lke x,y-coordnates, pressure, tme and pen velocty. Between sgnatures, the correlaton measure s determned. The dynamc features are extracted from authentc and forged sgnatures. Expermental results show that measurement of dynamc features (velocty changes) contans mportant nformaton and offers a hgh level of accuracy for sgnature verfcaton n comparson wth the results wthout such measurements, whch wll be explaned n the followng parts of the paper. Keywords: bometrcs; sgnature recognton; dynamc tme warpng; DTW; lnear regresson; dynamc measurements of features. Reference to ths paper should be made as follows: Doroz, R., Porwk, P., Para, T. and Wrobel, K. (28) Dynamc sgnature recognton based on velocty changes of some features, Int. J. Bometrcs, Vol., No., pp.47 62. Bographcal notes: Rafał Doroz receved hs MSc degree from the Slesan Unversty of Technology, Glwce, Poland n 999. Hs current research nterest areas are dgtal sgnal processng and bometrcs. Potr Porwk s an Assocate Professor (26) and works as Head of Computer System Department n the Insttute of Informatcs, Unversty of Slesa, Katowce, Poland. He teaches undergraduate and MBA courses n electroncs, computer networks and computer graphcs. Hs research areas of nterest nclude spectral analyss of Boolean functons, and most recently bometrcs, pattern recognton and mage processng. Tomasz Para and Krzysztof Wróbel receved ther MSc n Informatcs from the Unversty of Slesa n 24 and 999, respectvely. K. Wróbel receved hs PhD degree from the Unversty of Slesa n 26. Ther current research areas of nterest nclude dgtal mage processng, pattern recognton and computer graphcs. Copyrght 28 Inderscence Enterprses Ltd.
48 R. Doroz, P. Porwk, T. Para and K. Wrobel Introducton The sgnature features can be extracted from a gven statc mage of the sgnature and very often are classfed by means of global or local features. Global features descrbe an entre sgnature and can be determned by means of dscrete wavelet transform (Deng et al., 999), the Hough transform, horzontal and vertcal projectons (Porwk, 27). Local features descrbe personal features lke pen moton, slant, pressure, tremor n segments (Fang et al., 2; Fang et al., 23), and local shape descrptors (Doroz, 27). Varous pattern recognton technques have been proposed to authentcate handwrtten sgnatures. These technques are based on templates matchng (Jackson and Dunlevy, 988), mnmum dstance classfers (Sayeed et al., 26), neural networks (Baltzaks and Papamarkos, 2), hdden Markov models (HMMs) (Coetzer et al., 24), the Hough technque (Kaewkongka et al., 999; Coetzer et al., 24), the prncpal component analyss (PCA) (Al et al., 24), Teopltz matrces approach (Saeed, 23; Saeed and Albakoor, 26) or structural pattern recognton technques. Sgnature recognton methods can also be classfed as onlne or offlne, where approprate dynamc or statc sgnature features are extracted and analysed. These technques are well known n the research communty (Al-Shoshan, 26). In ths paper, onlne sgnature features have been verfed. For ths reason, dynamc features can also be extracted. In the statc method, where tme-relayed nformaton s not regstered (for example only btmap mage), these features are naccessble. Dynamc recognton captures the dstnct behavour characterstcs of an ndvdual s sgnature, such as the shape, speed, stroke, pen pressure and tmng nformaton. Some dynamc features were captured by means of the Topaz SgLte T-LBK75-HSB devce, whch s connected to a local computer. In order to acqure the date, the ndvdual must sgn ther unque sgnature multple tmes on the tablet. Ths devce allows the recordng of certfcated coordnates, tme and pressure n any (x,y) pont of a gven sgnature. Sgnatures, even those that belong to the same person, can dffer n many areas. Therefore, one of the most mportant aspects of sgnature verfcaton s the unfcaton process (pre-processng), whch allows the comparson of two sgnatures more precsely. In fact, there are a number of lmtatons n the data acquston phase. Frst, a sgnature cannot be too long or too short. If a sgnature s too long, then data analyss can be dffcult for the sgnature recognton system to dentfy the unque data ponts. In addton, pre-processng and recognton process are tme consumng. If a sgnature s too short, then the dataset wll not be representatve and the false accept rate (FAR) coeffcent wll be too large (for example, an mpostor can be authorsed by the sgnature recognton system). Second, the same person must complete the enrolment and verfcaton processes n the same type of envronment and under the same condtons. For example, f n the enrolment phase the ndvdual was standng, but durng the verfcaton phase he was sttng, then the captured sgnatures can substantally dffer from each other. After the data acquston phase, the dynamc sgnature recognton system extracts the unque features from the behavour characterstcs, snce sgnature recognton s classfed as behavour bometrc. The gven sgnature s descrbed by means of the unque features, whch are assgned to the sgner. Bometrc systems should be able to detect whether the sgnature s genune or forged. The results of the verfcaton depend on the type of the forgery. The frst type of a
Dynamc sgnature recognton based on velocty changes of some features 49 forgery s a random forgery and can be represented by a sgnature that belongs to any wrter (forger has no nformaton about the sgnature style and the name of the sgner). The second type - smple forgery s a sgnature whch has the same shape as the genune sgnature. The thrd type of the forgery s the so-called sklled forgery, whch s a professonal mtaton of the genune sgnature (Justno et al., 2). Offlne methods are used to detect random and smple forgeres. It follows from the fact that n ths approach only the shape of the sgnatures s consdered. Unfortunately, the offlne verfcaton method does not regster tmng nformaton and s not capable of modellng the handwrtng moton; therefore t s harder to recognse the genune sgnatures usng the offlne method. For ths reason, n ths paper dynamc features of the sgnature wll also be determned. Hence, t wll be possble to recognse forged and genune sgnatures. It wll be presented n the followng sectons of the paper. 2 Length sgnatures matchng The sgnatures can be treated as tme seres. In addton, sgnatures even from the same person can be dfferent. In statstcal methods of estmaton, these tme seres should have the same length. The dynamc tme warpng (DTW) technque overcomes ths lmtaton and gves ntutve dstance measurement (Salvador and Chan, 24). By means of DTW, algorthm optmal algnment between two tme seres can be acheved. Ths method s often used to match the sgnatures. The DTW s a smple technque well-known n the research communty. For ths reason, the detals of ths method were omtted. To algn two sequences usng DTW an n k costs matrx s constructed, where elements (,j) of the matrx contan the so-called cost values. The cost values determne the warp path. It matches sequences X(Y). The cost value s typcally computed as the dstance between two ponts, x X and y Y, respectvely. It should be stressed that between vectors of features, varous smlarty measures can be proposed: The Eucldean dstance: d( X, Y) = ( x y ) The Canberra dstance: 2 () x y d( X, Y) = (2) x + y The Chebyshev dstance: d( X, Y) = max( x y ) (3) The Manhattan (cty-block) dstance: d( X, Y) = ( x y ) (4) The defntons of dstance mentoned above wll be analyzed n the followng part of the paper.
5 R. Doroz, P. Porwk, T. Para and K. Wrobel 3 Sgnature rotaton In many cases, sgnatures even those that belong to the same person, have dfferent drecton and poston, hence t should be normalsed. Some technques normalse the sgnature poston by algnng the centres of the two sgnatures. Another approach s the transformaton of the sgnatures, so they have the same startng pont. Sgnatures can also be shfted towards Cartesans axes; ths method was used n ths paper. Sgnature drecton can be observed as a lne trend. In the pre-processng procedures, ths trend should be elmnated. In ths paper to elmnate the trend, the lnear regresson method was used. In the proposed approach, orthogonal regresson lne was ntroduced and compared to the other types of lnear regressons. It follows from statstcs that lnear regresson s a classc statstcal problem, where relatonshp between two random varables x and y should be determned. Lnear regresson attempts to explan ths relatonshp wth a straght lne to ft the data. There are three basc models of lnear regresson. The lnear regresson model postulates that: y = ax+ b (5) Straght lne (5) s estmated by means of the least-squares method. The coeffcents a and b are determned by the mnmsaton of some dstances and the sum of the squares of such dstances should be as small as possble. Because there are three basc models of lnear regresson, we must mnmse dfferent values (Fukunaga, 99): type- mnmse the vertcal dstances of the ponts from the lne type-2 mnmse the horzontal dstances of the pont from the lne type-3 mnmse the orthogonal (perpendcular) dstances from the data ponts (, ) the ftted lne (5): x y to n ( y ax b) Fab (, ) = 2 = a + where: n - the number of samples ( x, y ), =,..., n 2 The values a and b can be determned on the bass of the equatons: F F = and = a b Hence: (6) (7) F 2 2a n n 2 = ( y ax b) x ( y ax b) = 2 2 2 = ( ) = (8) a a + a + n F 2 = ( y ax b) = + 2 b a = For ths reason, after re-orderng, from the frst equaton (8), we have:
Dynamc sgnature recognton based on velocty changes of some features 5 2 2 2 2 2 2 s s + ( s s ) + 4 cov ( x, y) y x y x a = (9) 2 cov( x, y) where: the mean of samples: n x n = x =, n y n = y =, varance: s x x, n 2 2 x = ( ) n = s y y, n 2 2 y = ( ) n = covarance: n cov( x, y) = ( x x)( y y) n = From the second equaton (8): n n = = y a x nb = / n y ax b = b = y ax () Because the value a, determnes the slope of the lne (5), therefore a tan ( α ) =. The slope s drectly computed from equaton (9) and n the next operaton a gven sgnature can be approprately rotated (Foley et al., 993): new x = x cosα y sn α new y = x sn α + y cosα () where: ( x, y ) - a gven sgnature coordnates new new ( x, y ) - sgnature coordnates after rotaton The rotatons are presented n Fgure, where two examples of rotaton (the standard least square regresson and the orthogonal regresson) are depcted. Addtonally, after rotaton, a gven sgnature s approprately shfted accordng to the prncples: x = x x new mn y = y y new mn (2) where: x mn{ x,..., x } y = mn{ y,..., y }, mn = n, mn new new ( x, y ) - sgnature coordnates after shftng. n
52 R. Doroz, P. Porwk, T. Para and K. Wrobel Fgure (a) Two types of lne regresson: the least squares regresson (type-) and the orthogonal regresson (type-3), (b) sgnature after dsplacement and rotaton on the bass of the least squares regresson, (c) sgnature after dsplacement and rotaton on the bass of the orthogonal regresson 8 8 7 7 6 6 Y-coordnate 5 4 Y-coordnate 5 4 3 3 2 2 3 4 5 6 7 8 9 X-coordnate (a) 2 4 6 8 X-coordnate (b) 8 7 6 Y-coordnate 5 4 3 2 2 4 6 8 X-coordnate (c) 4 Sgnature smlarty measure The qualty of the ft measure between both sequences X,Y s well known as the Pearson factor R 2 (Le et al., 24). Instead of the two-dmensonal Pearson measure, a so-called ER 2 factor can be used, where m-dmensonal features vector can be analysed: ER 2 = m n ( x x )( y y ) j j j j j= = m n m n 2 2 ( x j xj ) ( yj yj ) j= = j= = 2 where: x j ( y j) s the average of the j-th dmenson of the sequence j ( j) (3) x y, respectvely. The factor ER 2 can be treated as the smlarty measure Sm between two multdmensonal sequences X and Y. The Sm coeffcent has smlar propertes lke R 2, hence values of smlarty are normalsed and Sm [,]. If Sm=, then both sequences
Dynamc sgnature recognton based on velocty changes of some features 53 X, Y have perfect lnear correlaton. If Sm=, then lnear relaton between sequences does not appear. In other words we obtan smlarty wth values between % %. In the proposed approach, approprate sgnature parameters are captured: x ( y) sgnature coordnates and pen pressure p. Qualty of the smlarty measure Sm was tested by means of 32 sgnatures. The approprate researches were carred out and the Sm measure was calculated by factors: False Rejecton Rate (FRR) - s stated as the rato of the number of false rejectons (NFRR) dvded by the number of total dentfcaton attempts T. 2 False Accept Rate (FAR) - s stated as the rato of the number of false acceptances (NFAR) dvded by the number of total dentfcaton attempts T. 3 Equal Error Rate (EER) - a pont where the FAR and FRR ntersect (the value of the FAR and the FRR at ths pont, whch s of course the same for both of them). Ths pont descrbes probablty when FAR and FRR are equal, the rsk of acceptng an mpostor s equally as small as the rsk of rejectng a legtmate user. 4 Effcency: T ( NFAR + NFRR ) Effcency = % (4) T In our case T=32. 5 Acceptance level: Because bometrc values are dffcult to model, ther statstcal sgnfcance s dffcult to estmate. In bometrcs, FAR/FRR factors are not standardsed, therefore these factors should be determned statstcally n costly tests. The FAR and FRR coeffcents are dametrcally opposed, for ncreasng FAR, the FRR coeffcent wll be decreased, and vce versa. It wll be easer f nformaton s consdered as yes/no (accept/reject) questons, for example the proxmty of a decson n the recognton algorthms va acceptance threshold level (Fgure 2). From equaton (3), t follows that f ER 2 factor s used, then both ( ) x y sequences should have the same length n. Takng nto account the mentoned consderatons, dfferent DTW costs values (warp path) were nvestgated. For dfferent rotatons (type-, type-2, type-3), the mean value EER was estmated. The concluson from the conducted nvestgatons s that the best result of sgnature matchng s acheved, f sgnatures wll be rotated by orthogonal regresson approach, and the Eucldean dstance wll be used. Thus, these parameters wll be used n future nvestgatons. The DTW algorthm s often adopted to speed up the method. Two of the most commonly used modfcatons are known as the Sakoe-Chuba band and the Itakura parallelogram (Salvador and Chan, 24). When constrants are used, the DTW algorthm fnds the optmal warp path nsde the defned wndow. Ths method s well known n the research communty, among other thngs ts descrpton s presented n detal n Salvador and Chan (24). In our nvestgatons, both methods of restrcton were checked. For the Sakoe-Chuba-type and Itakura constraned method, the obtaned results were better about
54 R. Doroz, P. Porwk, T. Para and K. Wrobel a, where the constant a s a value from any cell of the Table. Thus, n the next nvestgatons Sakoe-Chuba-type constran was mplemented because such DTW modfcaton s more convenent algorthmcally. Analysed data were extracted from the SVC 24 database, http://www.cse.ust.hk/svc24/ndex.html. Ths database ncludes 6 dfferent sgnatures. From the dataset, 32 sgnatures were randomly extracted. Table Influence of the DTW warp path determnaton on the EER factor of sgnature recognton system In DTW cost path determnaton (dstance) Wthout rotaton The mean EER factor (%) for dfferent types of regresson Type- Type-2 Type-3 Eucldean 3.7 9.8 3.43 3.39 Canberra 3.68 7.3 3.54 3.64 Chebyshev 3.94 9.82 3.84 3.84 Manhattan 3.4 8.97 3.83 3.5 Prepared database ncluded 5% genune and 5% forged sgnatures. The conducted experments can also be presented by means of the FAR-FRR-EER dagram (Fgure 2). The FAR and FRR values were determned by means of successve summaton of the probablty of dstrbuton FAR (FRR) curves that allows us to adjust the percentage acceptance level of the sgnature recognton system. Fgure 2 FAR FRR EER dagram 2 FAR FRR 8 Error Rate [%] 6 4 2 Note: EER=2.39% 2 4 6 8 2 Acceptance Level [%] If a system s secured, then the FAR and FRR wll vary n accordance wth the selected securty settng. In general, f FAR coeffcent decreases fast, then the system wll become more secure. Thus, the FRR s ncreased. A hgher FRR coeffcent s nconvenent because user authorsaton may requre addtonal access attempts. EER
Dynamc sgnature recognton based on velocty changes of some features 55 5 Recordng of sgnature features It was prevously mentoned that LBK75-HSB devce s able to capture many parameters of the sgnature. Ths devce s connected by means of the USB port and s automatcally recognsed by operatng systems (Wndows and Lnux). Experments were run on PC wth 52MB RAM and Celeron.6GHz. Any dscrete pont of the sgnature s descrbed by means of the values (x,y,p,t), and n calculatons, these values form features vector. Devce samplng frequency s f= 3kHz. Hence, pen sgnal s regstered wth frequency f by acquston devce and recordng s done along wth the tmng nformaton. Apart from values (x,y,p,t), regstered at the sgnature pont, two addtonal parameters were determned: pen velocty that changes along approprate axes and pen pressure that changes durng sgnng. Velocty changes were computed for approprate ndvdual trajectores: Δx( n) vx ( n) =, v Δ t y Δyn ( ) ( ) ( n) =, p Δ t ( ) Δpn v n = (5) Δ t Addtonally, the global velocty was also computed: Δ x( n) +Δy( n) vn ( ) g = Δt 2 2 where Δ x( n), Δ y( n), Δp( n) are the changes n the x, y, p drectons at dscrete pont n, respectvely. The new parameters were ncluded to sgnature features vector. The new elements of the features offer a good dversty level. Fgure 3 presents dfferent solated features of sgnature represented as tme seres. Tested sgnatures were extracted from the SVC24 database. For sklled-forgeres sgnatures, dfferences between genune and forged samples are presented n Fgure 4. Smple comparson of Fgures 3 4 shows that charts depcted n Fgure 4 are sgnfcantly dfferent than charts from Fgure 3 Fgure 5 presents genune sgnatures of two dfferent persons. In Fgure 5, t follows that dfferences between sgnatures can also be evdently observed. Experments have shown that veloctes measured along axes OX, OY and pressure features gve the best recognton level. In other words, these features contan the hgh qualty nformaton, unlke the shape nformaton. Durng features extracton, ther normalsaton s performed before attempt of the features matchng. Feature values can have dfferent ranges and f features have equal weght, these dstrbutons must be normalsed. For example, the dstrbuton of a gven feature has a range - to, whle another feature has a range from - to. If these features are matched wthout normalsaton, the matcher can gve nosed results. A large number of technques are used to value normalsaton, such as mn-max procedure, decmal scalng, medan, absolute devaton, etc. In the proposed nvestgatons, mn-max method was used. The mn-max normalsaton performs a lnear transformaton of the orgnal data and maps a value v of the feature a A to v new value n the defned range new new mna, max a : (6)
56 R. Doroz, P. Porwk, T. Para and K. Wrobel new v mna new new new v = ( maxa mna ) + mn a (7) max mn a new new where, n our case: mn =, max =. a a a Fgure 3 Two genune sgnatures of the same person presented by means of the tme seres (a) X-coordnate (b) Y-coordnate (c) P-pressure coordnate (d) pen velocty along OX axs (e) pen velocty along OY axs (f) pressure velocty 9 Sgnature Sgnature 2 7 Sgnature Sgnature 2 8 65 7 6 X-coordnate 6 5 Y-coordnate 55 5 45 4 4 3 35 2 9 8 2 3 4 5 6 7 8 9 Sgnature Sgnature 2 (a) 3 6 5 2 3 4 5 6 7 8 9 Sgnature Sgnature 2 (b) 7 4 P-pressure coordnate 6 5 4 3 Pen velocty along O X 3 2 2 - -2 2 3 4 5 6 7 8 9-3 2 3 4 5 6 7 8 9 8 Sgnature Sgnature 2 (c) 2 Sgnature Sgnature 2 (d) 6 Pen velocty along O Y 4 2-2 -4-6 Pressure velocty - -2-3 -4-8 2 3 4 5 6 7 8 9 (e) -5 2 3 4 5 6 7 8 9 (f)
Dynamc sgnature recognton based on velocty changes of some features 57 Fgure 4 Two sgnatures of the same person: genune (sgnature ) and forged sgnature (a) X-coordnates (b) Y-coordnates (c) P- pressure coordnates (d) pen velocty along OX axs (e) pen velocty along OY axs (f) pressure velocty Sgnature Sgnature 2 8 75 Sgnature Sgnature 2 9 7 8 65 X-coordnate 7 6 Y-coordnate 6 55 5 5 45 4 4 3 35 2 2 4 6 8 2 4 6 8 2 3 2 4 6 8 2 4 6 8 2 9 8 Sgnature Sgnature 2 (a) 6 5 Sgnature Sgnature 2 (b) 7 4 P-pressure coordnate 6 5 4 3 Pen velocty along OX 3 2 2 - -2 2 4 6 8 2 4 6 8 2-3 2 4 6 8 2 4 6 8 2 8 Sgnature Sgnature 2 (c) 2 Sgnature Sgnature 2 (d) 6 Pen velocty along OY 4 2-2 -4 Pressure velocty - -2-3 -6-4 -8 2 4 6 8 2 4 6 8 2 (e) -5 2 4 6 8 2 4 6 8 2 (f)
58 R. Doroz, P. Porwk, T. Para and K. Wrobel Fgure 5 The genune sgnatures of the dfferent persons (a) X-coordnate (b) Y-coordnate (c) P-pressure coordnate (d) pen velocty along OX axs (e) pen velocty along OY axs (f) pressure velocty 9 8 Sgnature Sgnature 2 8 75 Sgnature Sgnature 2 7 7 65 X-coordnate 6 5 Y-coordnate 6 55 5 4 45 4 3 35 2 2 4 6 8 2 4 6 8 2 3 2 4 6 8 2 4 6 8 2 9 8 Sgnature Sgnature 2 (a) 6 5 Sgnature Sgnature 2 (b) 7 4 P-pressure coordnate 6 5 4 3 Pen velocty along OX 3 2 2 - -2 2 4 6 8 2 4 6 8 2-3 5 5 2 8 Sgnature Sgnature 2 (c) 2 Sgnature Sgnature 2 (d) 6 Pen velocty along OY 4 2-2 Pressure velocty - -2-4 -3-6 -8 5 5 2-4 5 5 2 (e) (f) 6 Experments The proposed method of sgnatures preparaton was evaluated by usng the onlne sgnature database, Sgnature Verfcaton Competton (SVC) 24. In performed experments, the FRR, FAR and EER factors were determned. The FRR measures the rate of genune sgnatures classfed as forgeres, whle FAR represents the rate of forgeres recognsed as genune ones.
Dynamc sgnature recognton based on velocty changes of some features 59 Prepared database conssts of the 32 sgnatures. Ths database contrbutes to 6 genune sgnatures and 6 sklled mtatons that were wrtten by three forgers. So dataset ncludes 6 + 6 = 32 sgnatures. In the frst stage, the sgnatures were approprately rotated, standardsed by means of the DTW algorthm and normalsed. In the next stage, for each sgnature, ts dynamc features were determned. Investgatons were carred out for the three sets of the features,,,,,, x, y, p, v, v, v, v, respectvely. Investgaton results for, ( x y p) ( v v v v x y p g) and ( x y p g) were gathered n Table 2, where the EER factor for dfferent features of sgnature has been shown. Table 2 demonstrates that the best result of sgnature recognton gves the,,, x, y, p of the features gves the worst set ( vx vy vp vg) of the features. The set ( ) recognton level. Hence, the set (,,, x, y, p, g) x y p v v v v of the features gves also nsuffcent level of sgnatures recognton. Takng nto account presented consderatons, dynamc features based on velocty changes of parameters are very hard to forge, because they are never vsble. Obtaned results can also be presented n a graphc form. The approprate extracton of the sgnature features and ther comparson to other sgnature was determned durng.42ms. The comparson of the FAR/FRR dependences between dfferent sets of dynamc sgnature s presented n Fgure 6. The fgure presents two charts of two types of sgners: authentc and forged sgnatures. Table 2 Type of sgnature Effcency factor of the recognsng system ERR ( x, y, p ) ( v, v, v, v ) ( x, y, p, v, v, v, v ) x y p g Genune.43.4.4 Sklled forgery 3.75 3.39 3.54 x y p g Fgure 6 Comparson of the curves obtaned for proposed onlne sgnature verfcaton methods (a) genune (b) sklled-forgery sgnatures 4. X, Y, P V x,v y,v p,vg X, Y, P, V x,v y,v p,vg 6. X, Y, P V x,v y,v p,vg X, Y, P, V x,v y,v p,vg 3. 4. FRR 2. FRR. 2... 2. 3. 4. FAR. 2. 4. FAR 6. a) b) Table 3 gathers the most recently proposed technques, where pen tablets were used, n terms of ther ERR acheved factors. It should be stressed that t s unfar to compare technques based on dfferent sets of data and dfferent nput data devces; however the purpose of ths comparson s to show the effectveness of the proposed method.
6 R. Doroz, P. Porwk, T. Para and K. Wrobel Table 3 Dfferent onlne sgnature verfcaton methods Methods (for genune sgnatures) ERR (%) Proposed technque.4 Data glove, Kamel et al. (28) 2.37 Maramatsu et al. (23) 2.6 Kholmatov et al. (25) 2.8 Nakansh et al. (25) 3.3 Shnatro et al. (26) 4. Ferez-Agular et al. (25) from 5 to 7 Le et al. (24) 7.2 Source: Kamel et al. (28) The method proposed n ths paper also gves better results than that announced n Roja et al. (24), where dynamc features were analysed and neural network was used n sgnatures recognton. In the mentoned paper, authentc sgnatures and 5 forgeres were captured (+5=5). 98.2% orgnal sgnatures were recognsed (n our case 99.4%) and.8% forged sgnatures were not recognsed. (n our case 2.%). The number of tranng sgnatures has a strong nfluence on the resultng authentcaton system qualty. It should be notced that the database n the mentoned work was smaller than our dataset. In addton, n our nvestgatons the number of forged sgnatures was sgnfcantly larger. Sgnature verfcaton system should cope wth any attempts of recogntons, not only wth genune sgnatures. Moreover, the use of both forged and genune sgnatures allows us to obtan more objectve results. Usng only genune sgnatures provdes us wth too optmstc results (t even happens that FAR=FRR=%). It follows the fact that sgnatures of dfferent people are usually qute dfferent. So usually t s not a hard problem to dstngush such two sgnatures. The real problem turns up when the system tres to recognse very smlar sgnatures (especally skll-forged sgnatures). So t s hghly recommended to nclude forged sgnatures n nvestgatons. 7 Conclusons Although sgnature verfcaton s not one of the safest bometrc solutons, the use of t n the busness practces s stll justfed. In ths paper, t was consdered a problem of personal authentcaton through the use of sgnature recognton. In proposed nvestgatons, dfferent dynamc sgnature features have been analyzed. From nvestgatons, t follows that the best recognton level for genune and forged sgnatures s obtaned from the set of features, where velocty changes of onlne sgnature parameters are calculated. In addton, some operatons lke sgnature rotaton, DTW modfcaton and normalsaton process were also carred out. It was demonstrated that ths method can be used as a tool n handwrtng analyss. Takng nto account the mentoned nvestgatons, those types of dynamc features should be further developed.
Dynamc sgnature recognton based on velocty changes of some features 6 Acknowledgements We thank Prof. Vctor Lane (London) for readng the paper and for hs constructve comments. We would also lke to thank the anonymous referees and edtors for ther suggestons, whch helped to mprove the qualty of ths paper. References Al-Shoshan, A. (26) Handwrtten sgnature verfcaton usng mage nvarants and dynamc features, Proceedngs of the IEE Internatonal Conference on Computer Graphcs, Imagng and Vsualsaton, CGIV6, Sydney, Australa, pp.73 76. Al, G.N. et al. (24) Applcaton of prncpal components analyss and Gaussan mxture models to prnter dentfcaton, Proceedngs of the IS&Ts NIP2 Internatonal Conference on Dgtal Prntng Technologes, Salt Lake Cty, USA, Vol. 2, pp.3 35. Baltzaks, H. and Papamarkos, N. (2) A new sgnature verfcaton technque based on a two-stage neural network classfer, Engneerng Applcatons of Artfcal Intellgence, Vol. 4, pp.95 3. Coetzer, J., Herbst, B.M. and du Preez, J.A. (24) Offlne sgnature verfcaton usng the dscrete radon transform and a hdden Markov model, Journal on Appled Sgnal Processng, Vol. 4, pp.559 57. Deng, P.S. et al. (999) Wavelet-based offlne handwrtten sgnature verfcaton, Computer Vson and Image Understandng, Vol. 76, No. 3, pp.73 9. Doroz, R. and Wduch, St. (27) Sgnature characterstc ponts determnaton by means of the IPAN99 algorthm, Journal of Medcal Informatcs and Technologes, Vol., pp.5 3. Fang, B. et al. (23) Offlne sgnature verfcaton by the trackng of feature and stroke postons, Pattern Recognton, Vol. 36, pp.9. Fang, B. et al. (2) Offlne sgnature verfcaton by the analyss of cursve strokes, Internatonal Journal of Pattern Recognton and Artfcal Intellgence, Vol. 5, No. 4, pp.659 673. Foley, J. et al. (993) Introducton to Computer Graphcs, Addson-Wesley. Fukunaga, K. (99) Introducton to statstcal pattern recognton, 2 nd ed., Academc Press, USA. Jackson, J.D. and Dunlevy, J.A. (988) Orthogonal least squares and the nterchangeablty of alternatve proxy varables n the socal scences, The Statstcan, Vol. 37, No., pp.7 4. Justno, E.J.R., Bortolozz, F. and Sabourn, R. (2) Offlne sgnature verfcaton usng HMM for random smple and sklled forgeres, Proceedngs of the 6th IEE Internatonal Conference on Document Analyss and Recognton, ICDAR 2, Seattle, USA, pp.45 453. Kaewkongka, T., Chamnongtha, K. and Thpakorn, B. (999) Offlne sgnature recognton usng parametersed Hough transform, Proceedngs of the 5th Internatonal Symposum on Sgnal Processng and ts Applcatons, Brsbane, Australa, pp.45 454. Le, H. et al. (24) ER2: an ntutve smlarty measure for onlne sgnature verfcaton, 9th Internatonal Workshop on Fronters n Handwrtng Recognton - IWFHR4, Tokyo, Japan, pp.9 95. Kamel, N.S., Ells, G.A. and Sayeed, S. (28) Glove-based approach to onlne sgnature verfcaton, IEEE Transactons on Pattern Analyss and Machne Intellgence (n press). Porwk, P. (27) The compact three stages method of the sgnature recognton, Proceedngs of the 6th IEEE Internatonal Conference Computer Systems and Industral Management Applcatons, CISIM7, Ełk, Poland, pp.282 287. Roja, F.R. et al. (24) Dynamcs features extracton for onlne sgnature verfcaton, Proceedngs of the 4th IEE Internatonal Conference on Electroncs, Communcatons and Computers, Veracruz, Mexco, pp.56 62.
62 R. Doroz, P. Porwk, T. Para and K. Wrobel Saeed, K. (23) Object classfcaton and recognton usng Toepltz matrces, Proceedngs of the Internatonal Conference on Artfcal Intellgence and Securty n Computng systems, Kluwer Publshers, Massachusetts, USA, pp.63 72. Saeed, K. and Albakoor, M. (26) Experments on Toepltz matrx egenvalues n mage recognton: four approaches on Arabc scrpt wthout thnnng, Proceedngs of the 3 th Internatonal Mult-conference on Advanced Computer Systems, CS-AIBITS/CISIM6, Poland, pp.7 7. Salvador, S. and Chan, P. (24) Fast DTW: toward accurate dynamc tme warpng n lnear tme and space, Proceedngs of the Internatonal Conference on Knowledge Dscovery and Data Mnng - KDD4, Seattle, USA, pp.7 8. Sayeed, S., Besar, R. and Kamel, N.S. (26) Dynamc sgnature verfcaton usng sensor based data glove, Proceedngs of the 8 th Internatonal Conference on Sgnal Processng, Bejng, Chna, Vol. 3, pp.2387 239. Sgnatures database. Avalable onlne at http://www.cse.ust.hk/svc24/ndex.html