Some Handwritten Signature Parameters in Biometric Recognition Process

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1 Some Handwritten Signature Parameters in Biometric Recognition Process Piotr Porwik Institute of Informatics, Silesian Uniersity, Bdziska 39, 41- Sosnowiec, Poland Tomasz Para Institute of Informatics, Silesian Uniersity, Bdziska 39, 41- Sosnowiec, Poland Abstract. In tis paper tere is te off line type signature analysis profoundly considered. Te analysis consists of tree stages wic allow to define te features (weigts) of te signature. Different influences of suc features are tested and stated. In tis paper personal signature is first pre-processed and ten processed in te tree-stage metod. In proposed approac te Houg transform is introduced, te centre of signature graity is determined, and te orizontal and ertical signature istograms are performed. Proposed approac gies good signature recognition leel, ence described metod can be used in many areas, for instance in biometric autentication, eiter as biometric computer protection or as a metod of te analysis of person s beaiour canges. Keywords. Signature recognition, Houg transform, pre-processing, features extraction 1. Introduction Te signature recognition is te process aiming at writer s erification. During tis process te samples of signatures are compared wit te oter ones gatered in te database records. Te signature recognition is one of many biometric identification tecniques wic are used in practice. In te business world we sign different papers suc as accounts and oter official documents. Personal signature lends itself well to biometric erification in state of te art electronic deices. Numerous metods and approaces are summarised in a number of surey articles [1,,4]. Signature recognition can be realised by means of static or dynamic metods [1]. Static metods, based on bitmap image analysis, do not require specialised deices, for example pressure-sensitie pens and surfaces, ence are willingly used. Dynamic metods, were time, stroke, speed and signature pressure are additionally recorded, are expensie and signature capturing is ery often uncomfortable. It sould be stressed tat dynamic metods are difficult to forge [1,4]. In our approac new signature parameters are also determined were centre of signature s istogram was computed and proportion factor was establised. Proposed metod gies better results of recognition and erification comparing wit metods described in [1,4]. Complete inestigation results could not be included as oer 8 signatures from database were tested. One drawback of signature is tat people do not sign in exactly te same manner. For example, te angle at wic tey sign may be different due to seating position or due to and placement on te writing surface. For tis reason, te original signature sould be appropriately formatted and pre-processed. In our approac, te signature analysis process is composed of tree main stages: pre processing: were image binarization and its size standardization are performed, feature extraction: were te unique set of caracteristics of te analysed signature is gatered, comparison: were personal signature is compared wit te pattern from te signatures database.. Pre-processing A wide ariety of deices capturing signature causes te need to normalize an input image of signature (so called: pre-processing). Te pre processing procedure consists of tree steps: binarization, cutting edges, tinning..1. Binarization It allows us to reduce te amount of image information (remoing colour and background), so te output image is black-wite. Te blackwite type of te image is easier to furter processing. 185 Proceedings of te ITI 7 9 t Int. Conf. on Information Tecnology Interfaces, June 5-8, 7, Catat, Croatia

2 .. Cutting edges Size of te image is reduced. In tis procedure unnecessary signature areas are remoed. In oter words, we find te max/min alue of te X and Y coordinates of te signature (Fig.1) and ten te image is cut to te signature size. It allows to reduce te total number of te pixels in te analysed image (Fig. )..3. Tinning Figure 1. Input image Figure. Reduced image size It allows us to form a region-based sape of te signature. It sould be noticed tat main features of te object are protected. After tinning, te 1-pixel sape of signature is obtained. Good results of image tinning can be acieed from so called Palidis algoritm [3]. 3. Features extraction During tat step a gatering of caracteristic data takes place. Te output result is a set of te unique information about te signature. Actions occurring during tat step supply: proportion factor, ertical and orizontal projection, centre of graity, te Houg transform Proportion factor Proportion factor defines te relation between widt w and eigt of te different personal signatures, wic are signed by te same person. Value of te proportion factor is calculated by te formulas: w γ = if w (1) γ = if w < () w 3.. Vertical and orizontal projection Tis metod describes te ertical and orizontal signature pixels density (istogram). Te istogram is obtained in two-passes algoritm, were te number of signature s pixels in eac row and in eac column is counted. Obtained results are stored in te two one-dimensional auxiliary tables T (for ertical part of te image) and T (for orizontal part of te image). After te data collecting, appropriate image projections are calibrated to resolution of pixels. In te first stage te calibration coefficient is calculated: max{ XY, } δ = (3) 56 In te next stage two normalized projection arrays N i N are prepared: T [ round ( i * δ )] N [ i] = round( ), i =,..., 55 (4) δ T [ round( i * δ)] N [ i] = round ( ), i =,..., 55 (5) δ Applying te size normalized calibration approac allows to compare te image s projections for different size of signatures Centre of graity It supplies information about te layout of pixels density. It is a point G(x g,y g ) were appropriate lines A and B are crossing, wat presents Fig. 3. Tese lines diide te signature image into ertical and orizontal regions so tat te number of pixels was te same in eac region. Te coordinates (x g,y g ) are obtained basing on analysis of te ertical and orizontal projection arrays N and N., respectiely. Te alue of te coordinate x g is equal to suc index k x of te cell of te N array, for wic te next condition is fulfilled: N [] i N [] i (6) kx 1 kx i= i= N [] i < N [] i i= i= 186

3 Figure 3. Signature centre of graity Te alue of te coordinate y g is equal to suc index k y of te cell of te N array, for wic te next condition is fulfilled: N [] i N [] i (7) ky 1 ky i= i= N [] i < N [] i i= i= 3.4. Te Houg Transform In te last stage te Houg Transform (HT) is used [1]. Tis algoritm searces a set of straigt-lines, wic appear in te analyzed signature. Te classical transformation identifies straigt-lines in te signature image but it as also been used to identifying te signature sapes. In te first step te HT is applied, were appropriate straigt-lines are found (Fig.4). Te analyzed signature consists of large number of straigt-lines, wic were found by te HT, ence reduction of te unnecessary lines sould be carried out. For tis reason additional straigt-line selection algoritm is applied [4]. In tis algoritm some lines are remoed and te set of reduced straigt-lines can be performed. It can be obsered (Fig. 4) tat a lot of straigt-lines are related ery close to eac oter and are quite similar (sligtly different at angles and positions). Te straigt-line selection algoritm remoes suc lines. Te range of te lines reduction by experiments is matced, were tresolding procedure is applied [1,4,6]. A result of te straigt-line reduction presents Fig.5. Te Houg Transform is well known in te researc community, terefore teir details will be omitted. In te next step, te set of te reduced straigt lines is excanged for appropriate sections by means of te backpropagation algoritm [5,6]. Te set of te sections (Fig.6) is analyzed again and te sections lying along te same direction are connected (Fig. 7). Suc step allows to reduce number of signature features. Figure 5. Te reduced number of te lines Te result of te canges presents Fig. 6. Figure 6. Te sections extracted from lines Figure 7. Joined sections Additionally, for eery section l i teir (x i,y i ) coordinates are stored in auxiliary table. Finally, te sections image is calibrated to te pixel-size image. It allows to compare signatures, wic originate from different sources and ae different sizes (Fig.8) Figure 4. Straigt-lines extracted from te image (Fig.) by means of te HT Figure 8. Te normalized size of te image from te Fig. 7 Te reduced set of sections is te most unique feature, wic describes te signature. It will be sown in te conclusie inestigations. 187

4 4. Determining of te pattern signature For recognition process, te input and genuine signatures sould be known. In tis process, te unique features (patterns) of eac signature are compared wit analyzed input sign. For tis reason, te patterns of te genuine signature are stored in a database. Tese patterns would contain all caracteristic features of signature. Unfortunately, signatures of te same person ae some differences. So it is needed to build a pattern, wic coers tese differences. In proposed approac, a procedure tat determines similarity between signatures S 1 and S was implemented. As an input data te two sets of unique features of te signatures S 1 and S are analyzed. Te first set Ω S1 includes all straigt-lines, wic were found in te signature S 1 (person signature). Te second set Ω S includes all straigt-lines, wic were found in te signature S (from database). Te result of te comparison is te global signature similarity coefficient s. During te first step te straigtline similarity coefficient is determined. Eac line i-t from te first set Ω S1 is compared wit te adequate j-t line wit te most appropriate coordinates in te second set Ω S. Te basic principle of te lines comparison presents Fig. 9. From tis figure follows, tat te i-t straigtline as coordinates ( B, E ) B 1 1 1( x, y ), E 1 1 1( x, y ). Te b b e1 e1 appropriate j-t straigt-line as coordinates ( B, E ) B ( x, y ), E ( x, y ). Hence, te b b e e partial similarity coefficient i can be calculated from te formula: Δ B+ΔE ε i = 1 (8) Δ B= ( x x ) + ( y y ) (9) b1 b b1 b Δ E = ( x x ) + ( y y ) (1) e1 e e1 e were: Δ B distance between beginning of te i-t and j-t straigt-line coordinates, Δ E distance between end of te i-t and j-t straigt-line coordinates In te next stage te j-t straigt-line is remoed from te second set and te next straigt-line from te first set is analyzed. After tat, te algoritm is repeated in just opposite way (te lines from second set are compared to line from te first set) and we receie j coefficient. Figure 9. Basic principles of te comparison of te two different straigt-lines All partial coefficients are summarized and te mean alue is calculated: ε + ε i j i j s s = (11) were i = card( Ω ) and j = card( Ω ). S1 S Te next stage of our algoritm determines te projection similarity coefficient. Data about orizontal and ertical projection are stored in two one-dimensional 1 56 arrays: N and N. Usually projections of two images are sligtly sifted to eac oter. For tis reason projections are compared to eac oter many times wit some deiation d= ± 1 pixels. Te results are stored in te arrays T and T for te ertical and orizontal projection, respectiely: T [] i = N [] i N [], i i =,...,55, 1, (1) T [] i = N [] i N [], i i =,...,55, 1, were: N1,, N ertical and orizontal projection 1, arrays, for te signature S 1, N,, N ertical and orizontal projection, arrays, for te signature S from te database. and ten te partial similarity coefficients ( and ) are determined for eac table: 55 T [] i σ = 1 i= 56 (13) 55 T [] i σ = 1 i= 56 (14) Te global projection similarity coefficient is calculated by te formula: max{ σ d,,..., σd, } + max{ σ d,,..., σd, } s p = (15) 188

5 In te last stage te proportion similarity coefficient s r is calculated: γ γ 1 s r = 1 (16) and centre of graity similarity coefficient s g : ΔG sg = 1 (17) were G distance between coordinates of centre of graity G 1 (x g1,y g1 ) for signature S 1 and centre of graity G (x g,y g ) for te signature S from te database, respectiely. Finally, te global similarity coefficient is calculated by using te following formula: s = sp + s p + sp + sp (18) s s p p r r g g were : s s sections similarity coefficient, s p projection similarity coefficient, s r proportion similarity coefficient, s g centre of graity similarity coefficient. Aboe mentioned coefficients are formed by comparing eac feature from one set wit corresponding feature from te oter set. Finally, te appropriate similarity coefficients are calculated. For eery similarity coefficient s, appropriate feature weigt p s, p p, p r, p g is selected and additionally, te condition p s +p p +p r +p g =1 as to be fulfilled. Te weigts alues were empirically determined. It was establised, tat weigts, were te best participation of te features were performed, ae alues: p s =.54, p p =.3, p r =. and p g =.1. Wen te similarities procedure is already implemented, it is possible to build a signature patterns. Te patterns are determined on te basis of a few (say tree) signatures of te same person. Suc signatures sould be collected at different day time, during te wole week. At te next stage, features of te tree signatures are compared wit eac oter. As te pattern is cosen tis signature tat as te igest global similarity ratio (i.e. tat is te most similar to oters signature), tat pattern and its caracteristic features, are stored in te database. Using tat pattern can be performed for all future comparisons. About 8 signatures were collected in our own database. All signatures were stored as bitmaps. From database, 8 signatures were randomly cosen. Eac signature was collected four times ( signatures session wit an interal of two weeks). On te basis of 8 4=11 signatures efficiency of te proposed metod were tested. From 4 signatures of te same person global features were extracted and stored in te database. 5. Signature erification and identification Tere are two areas of application for signature recognition systems: Verification were te input signature (and its caracteristic features) is compared wit one pattern from te database and judging if tese signatures are te same or not. Identification were te corresponding pattern in database is searced until te one matces te input signature. Bot metods aboe use global similarity coefficient and global tresold alue [,5]. Te erification and identification are successful if te similarity for a tested signature is greater or equal to global tresold alue. Te global tresold alue bases on te formula: t = (1 ψ )( t p + t p + t p + t p ) (19) were: t s, t p, t r, t g p s, p p, p r, p g ψ s s p p r r g g partial tresolds for te elements of te pattern (set of sections, projection, proportion factor, centre of graity) importance (weigt) for eac feature tolerance coefficient Te global tolerance coefficient decreases (increases) all partial tresolds t s, t p,t r, t g of k %. For example if =k% ten t s = t p =t r = t g =k%. Te tolerance coefficient as some considerable influence on te final result of erification or identification. If condition s t is fulfilled ten signatures identification process is positie, oterwise if s < t identification is negatie compared signatures belong to different persons. 6. Inestigation results In te inestigations, caracteristic features (set of sections, projection, proportion coefficient, centre of graity) ae been tested separately and te influence of te eac feature as been obsered. Te test gies information about canges coefficients (False Accept Rate) and (False Reject Rate). Te typically is stated as te ratio of te number of false acceptances (N ) diided by te number of total identification attempts T. Te is stated as te ratio of te number of false rejections (N )diided by te number of total identification attempts T. 189

6 For identification mode T=11 signatures were analyzed and N =, N =6. For erification mode, for we ae T 1 =4 7 8=34, N =119. For, T =11, N =6. T=T 1 +T =3136. Hence: T ( N + N ) Efficiency = 1% () T Experiments are carried out to estimate te performance of utilizing proposed approac in a combined matcing system. Obtained results ae been sown in Table 1. Retrieing time for one signature is 39ms for identification mode, and 99ms for erification mode (PC AMD Atlon 1.91GHz, RAM 51MB). Table 1. Comparison of te two modes of te signature recognition Identification (%) Verification (%) Efficiency Efficiency 1,79 3,57 94,6 3,94 5,36 96, / (%) / (%) Efficiency (%) Identification Global tresold (%) Verification Global tresold (%) Identification Efficiency Tolerancy (%) Efficiency (%) Conclusions Verification Efficiency Tolerancy (%) A fundamental problem in te field of offline signature erification is te lack of any pertinent sape factors. Te main difficulty in te definition of pertinent features lies in te local ariability of te signature line, wic is closely related to te intrinsic caracteristic of uman beings. In tis paper a new combined metod of signature analysis as been proposed, were extraction of signature sections, its proportion, istograms and te centre of graity are stated. Experimental ealuation of tis sceme as been made using a signature database, wic included 8 genuine signatures. Tis experiment confirmed tat te proposed metod is efficient and its effectieness leel is ery attractie.. References [1] Coetzer J., et al. Offline Signature Verification Using te Discrete Radon Transform and a Hidden Marko Model. EURASIP Journal on Applied Signal Processing 4, 4:, [] Kaewkongka T., et al. Off-line signature recognition using parameterized Houg transform. Proc. of te 5t Int. Symp. on Signal Processing and its Applications. 1999, 1:, [3] Palidis T. A tinning algoritm for discrete binary images. Computer Grapics and Image Processing 198, 13: [4] Sabourin R., Genest G., Preteux F. Pattern Spectrum as a Local Sape Factor for Off- Line Signature Verification. Int. Conf. Pattern Recognition, Austria, Vienna, 1996; 3: