JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 13/009, ISSN 164-6037 Krzysztof WRÓBEL, Rafał DOROZ * fingerprint, reference point, IPAN99 NEW METHOD FOR FINDING A REFERENCE POINT IN FINGERPRINT IMAGES WITH THE USE OF THE IPAN99 ALGORITHM This stuy presents a new metho for fining a reference point in fingerprint images. The propose metho is base on the IPAN99 algorithm, which etects high curvature points on a contour of a graphical object. This algorithm was ajuste in the stuy to etect high curvature points on friction riges. It allows locating a reference point on a fingerprint image. Since the IPAN99 algorithm requires that the thickness of an analyse contour shoul be of one pixel, each fingerprint image was aequately prepare before submitting to the analysis with the IPAN99 algorithm. Evaluation of the efficiency of the metho consiste in comparing the istances between coorinates of reference points etermine with the use of the propose metho an inicate by an expert. The evelope metho was compare with other algorithms use for etermining a reference point. 1. INTRODUCTION The methos of verifying people s ientity base on fingerprint recognition are becoming some of the safest methos of authentication [5,6,7,1]. They prove their usefulness in the era of very high requirements set for security systems. This results to a large extent from the fact that biometric ata cannot be stolen or lost. A reliable system of person ientification an verification allows avoiing errors that case material losses as well as a loss of confience in a company or an institution. Fingerprint images contain friction riges. After aequate transformations, friction riges can be treate as sequences of points with specific coorinates, an can be processe with the use of image analysis methos. An analysis of unique patterns (minutiae), which form friction riges, allows establishing people s ientity [6]. Two images of fingerprints of the same finger taken in a certain time interval are nearly always slightly ifferent - they will be shifte, rotate through a certain angle, etc. Therefore, many methos for recognizing people on the basis of fingerprints require fining a reference point on the fingerprint, aroun which minutiae are being analyse [5,10,11]. This reference point is efine as a place on a fingerprint, in which the curvature of friction riges is the highest. In this stuy, the IPAN99 algorithm, which etects high curvature points on a contour of a graphical object, was use for fining a reference point [1]. The usability of this algorithm in the process of etermining a reference point on fingerprint images has not been stuie so far.. IMAGE PREPARATION The fingerprint images teste within the stuy containe friction riges with a thickness higher than one pixel. Since the IPAN99 algorithm requires that the thickness of a contour being analyse shoul be of one pixel [1], the teste images were aequately prepare. In the first phase, the fingerprint images were submitte to an operation that aime at improving their quality [4,6,9]. This operation consiste of a few stages, which were iscusse in etail in other stuies. In the presente stuy, there was use a quality improvement metho, which gave a binary image as a result [9]. Sample fingerprint images before an after the operation improving their quality are presente in Fig. 1. University of Silesia, Institute of Computer Science, 41-00 Sosnowiec, Bęzińska 39, Polan, krzysztof.wrobel@us.eu.pl, rafal.oroz@us.eu.pl
) e) f) Fig. 1. Sample fingerprint images before a-c) an after -f) the operation improving their quality. Thanks to the operation improving the quality of the analyse images, the visibility of friction riges was better, which facilitate the further analysis. In the last stage, the fingerprint images were submitte to a skeletonization process. There was use the Pavliis algorithm escribe in the stuy [8]. Sample images after the skeletonization are presente in Fig.. Fig.. Sample images after the skeletonization. Having the images prepare in such a way, each friction rige coul be recore as a sequence of points with specific coorinates. In this stuy, the metho escribe in the paper [3] was use for fining the sequence of points. During the research work it appeare that the IPAN99 algorithm ha etecte high curvature points in bifurcations of friction riges. This coul result in an incorrect etection of a reference point. Examples of incorrect etection of reference points in bifurcations of friction riges are presente in Fig. 3. These points were marke with a circle on the rawings. The reference point inicate by an expert were aitionally labelle with a cross mark. Fig. 3. Fragments of fingerprint images with some incorrectly etecte reference points (circle) an the reference points inicate by an expert (cross). In orer to prevent the above-mentione situation, moifications were mae, which consiste in removing a fragment of an image with a size of k k pixels, containing a given bifurcation. A sample image fragment before an after removal of all bifurcations is shown in Fig 4. The value of the k parameter was 5. 60
a) b) Fig. 4. Sample fragment of a fingerprint image: a) before removal of bifurcations, b) after removal of bifurcations. As a result of using the above methos, a set of curves representing friction riges was obtaine for each fingerprint. These curves ha a thickness of one pixel, an in the further part of the stuy were analyse with the use of the IPAN99 algorithm in orer to fin high curvature points. 3. IPAN99 ALGORITHM Detection of the highest curvature points with the use of the IPAN99 algorithm takes place in two stages [1]. In the first stage, the point p of the curve is taken as a corner, if it is possible to inscribe a triangle with a specific opening angle an ifferent lengths of sies (p, p, p + ) in this curve (Fig. 5). p + c p - a α p b p v ( ) α p v α( p) p Fig. 5. Detection of the highest curvature points base on the IPAN99 algorithm. Triangles are constructe accoring to the following conitions: where: min α max max + min max p p (1) min max parameter specifying the minimum length of triangle sies, parameter specifying the maximum length of triangle sies, p p () α α max (3) critical angle, which etermines the value of the angle of a triangle inscribe in the curve in a given point in orer to classify this point as a caniate for a corner. + p p = a = a istance between p an p + points, p p = b = b istance between p an p - points, + p p = c = c between p + an p - points, [ π, π ] α opening angle of a triangle, efine as follows: a + b c α = arccos (4) ab Inscribing a triangle at any point p is starte with etermining the smallest possible lengths of triangle sies. Then, next triangles are create by increasing the lengths of their sies. The algorithm is stoppe, if a triangle oes not meet one of the conitions 1-3. From among all acceptable triangles in a given point p, the triangle with the smallest opening angle α(p) is selecte. In the secon stage, the point p is rejecte, if in its neighbourhoo there is a point p ν, which has a smaller opening angle: 61
The point p ν belongs to the neighbourhoo of the point p, if it fulfils the conition α(p) > α(p ν ) (5) p p. v min Depening on the values of the min, max, α max parameters, the IPAN99 algorithm etecte a ifferent number of high curvature points on each friction rige. The result of operation of the algorithm was the n set of {p 1, p,, p n } points for each friction riges on a single fingerprint. For the nees of this stuy it has been accepte that the p r reference point is efine as follows: pr = min( α( p i )) for i = 1,..., n (6) The reference points on fingerprint images etermine with the use of the IPAN99 algorithm, as well as the reference points inicate by an expert are shown in Fig. 6. Fig. 6. Example of operation of the IPAN99 algorithm on fingerprint images. 4. THE COURSE AND RESULTS OF THE STUDIES The tests were performe on a set of images coming from FVC atabase []. Sample test images are presente in Fig. 7. ) e) f) g) h) Fig. 7. Sample images use in the tests. Evaluation of the efficiency of the metho consiste in comparing the coorinates of reference points etermine with the use of the propose metho an inicate by an expert. Eucliean istance were etermine between the points being compare. On the basis of the values of the istance, the efficiency of the metho was evaluate. For the IPAN99 algorithm, the following parameters were experimentally selecte: min =17, max =19, α max =150. The value of the k parameter was set at 5 pixels. Aitionally, the results of the tests were compare with other methos. The results of tests for the sample fingerprints presente in Fig. 7 were given in Table 1. 6
Table 1. Results of the stuies B C A D Algorithm 1 Algorithm Expert s Distance Distance Our metho Distance Image 1 1 1 assessment p r = xr, y r D1=A-B pr = xr, y r D=A-C new new, new p e e, e r xr y D3=A-D p = r r = xr y r [11] [10] a) [18, 310] [0, 301] 9, [14, 313] 5,00 [5, 307] 7,6 b) [13, 87] [130, 74] 13,15 [137, 91] 6,40 [111, 81] 1,84 c) [137, 301] [139, 83] 18,11 [148, 30] 11,05 [154, 300] 17,03 ) [14, 85] [130, 74] 1,53 [137, 91] 14,3 [13, 80] 9,43 e) [149, 73] [148, 65] 8,06 [159, 69] 10,77 [155, 69] 7,1 f) [159, 357] [157, 346] 11,18 [170, 357] 11,00 [163, 340] 17,46 g) [163, 45] [166, 38] 7,6 [181, 47] 18,11 [176, 4] 13,34 h) [05, 46] [193, 38] 14,4 [03, 47],4 [04, 45] 1,41 An analysis of the results presente in Table 1 shows that in three cases (, e an h images), the propose metho gives results more close to the expert s inications than other methos compare with it. Also in three cases, the propose metho gives results more close to the expert s inications than one of other methos ( a, c an g images). Whereas in other cases, the results are worse than in the compare methos. 5. CONCLUSIONS The conucte stuies emonstrate that the presente metho is usable for fining a reference point. The obtaine results are comparable with other methos an only slightly iffer from expert s inications. Next stages of the research will involve tests performe on atabases containing a larger number of fingerprint images an in ifferent quality. Other parameters of the IPAN99 algorithm will be teste too. It is planne to evelop new methos that allow eliminating incorrectly etecte high curvature points within bifurcations an other types of minutiae. BIBLIOGRAPHY [1] CHETVERIKOV D., SZABO Z., Detection of high curvature points in planner curves. In 3r Workshop of the Austrian Pattern Recognition Group, pp. 175 184, 1999. [] Fingerprint Verification Competition: http://bias.csr.unibo.it/fvc006 [3] GOŁUCH P., Charakteryzacja konturu D przy pomocy punktów brzegowych o największej krzywiźnie (in polish), praca licencjacka, promotor: W. Kotarski, Uniwersytet Śląski, Sosnowiec, 003. [4] GREENBERG S., ALADJEM M., KOGAN D., DIMITROV I., Fingerprint image enhancement using filtering techniques. Proceeings of the 15th International Conference on Pattern Recognition (ICPR 00), Vol. 3, pp. 3 35, Barcelona, Spain, 000. [5] JAIN A.K., PRABHAKAR S., JONH L., PANKANTI S., Filterbank-base fingerprint matching. IEEE Transactions on Image Processing, Vol. 9, No. 5, pp.846 859, 000. [6] MALTONI D., MAIO D., JAIN A.K., PRABHAKAR S., Hanbook of Fingerprint Recognition. Springer professional computing series, NY, 003. [7] PARK U., PANKANTI S., JAIN A.K., Fingerprint verification using SIFT features. SPIE Defense an Security Symposium, paper 6944-19, Orlano, USA, 008. [8] PAVLIDIS T., A thinning algorithm for iscrete binary images. Computer Graphics an Image Processing, Vol. 13, pp.14 157, 1980. [9] PORWIK P., WIĘCŁAW Ł., The New Efficient Metho of Fingerprint Image Enhancement. International Journal of Biometrics, Vol. 1, No. 1, pp. 36-46, Inerscience Publisher, 008. [10] PORWIK P., WIĘCŁAW Ł., Fingerprint Reference Point Detection Using Neighbourhoo Influence Metho. Avances in Intelligent an Soft Computing, Vol. 45, Computer Recognition Systems, pp. 768-794, Springer-Verlag, 007. [11] PORWIK P., WRÓBEL K., The new algorithm of fingerprint reference point location base on ientification masks. Avances in Intelligent an Soft Computing, Vol. 30, Computer Recognition Systems, pp. 807-814, Springer-Verlag, 005. [1] WANG R., BHANU B., Preicting fingerprint biometrics performance from a small gallery. Pattern Recognition Letters, Vol. 8, No. 1, pp.40 48, 007. 63
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