Adaptation of Rigid Registration Algorithm to the Fingerprints Identification

Transcription

1 Amerian Journal of Software Engineering and Appliations 205; 4(6): 07-4 Published online Otober 8, 205 ( doi: 0.648/j.ajsea ISSN: (Print); ISSN: X (Online) Adaptation of Rigid Registration Algorithm to the Fingerprints Identifiation Mostafa Boutahri *, Samir Zeriouh, Said El Yamani, Abdenbi Bouzid, Ahmed Roukhe Optroni and Information reatment eam, Atomi, Mehanial, Photoni and Energy Laboratory, Faulty of Siene, Moulay Ismail University, Zitoune, Meknès, Moroo address: (M. Boutahri) o ite this artile: Mostafa Boutahri, Samir Zeriouh, Said El Yamani, Abdenbi Bouzid, Ahmed Roukhe. Adaptation of Rigid Registration Algorithm to the Fingerprints Identifiation. Amerian Journal of Software Engineering and Appliations. Vol. 4, No. 6, 205, pp doi: 0.648/j.ajsea Abstrat: In this paper, we present an automated system for the reognition and identifiation of fingerprints based on rigid registration algorithms. Indeed, after preproessing arried on a fingerprint database olleted in the laboratory, we have built maps of minutiae for eah fingerprint. Subsequently, we applied a rigid registration algorithm based on iterative searh for losed points ICP (Iterative Closest Point), whih allowed us to ompare shifted fingerprints serving as test with the fingerprints of the referene database. his omparison gives onvining results and shows high auray. Keywords: Reognition, Identifiation, Fingerprints, Rigid Registration, Minutiae, ICP. Introdution Nowadays, authentiation beomes one of the essential points at the seurity aess ontrols in informatis systems. It is based on the reognition that verifies the presumed user identities []. he identifiation is generally based on several riteria and aording to the extent of the investigation. It may be an identifiation by DNA searh, by using images from surveillane ameras, for removal of odors and sents or most often by use of fingerprints [2]. his proess remains in our day, the most reliable and most ost espeially that the polie have fingerprint database of all itizens holder a national identity ard. he important progress experiened by the fingerprint verifiation area in reent years have required new image proessing tools, espeially for multimodal image (from different soures). he registration algorithms are part of these tools. here are many, both within the rigid or non-rigid registration. However, these algorithms are diffiult to validate in the absene of a referene standard for verifying the fields obtained. In this ontext, our work proposes adapting rigid registration algorithm variants, allowing a more effetive omparison. Indeed, after sanning fingerprints, objet of study, we onduted a binarization and thinning of fingerprints to finally build the maps orresponding to eah minutiae fingerprint. Subsequently, we established a mathematial modeling of the registration problem to fit these data based on iterative method ICP. he simulation results obtained after several tests resulted in the reognition of fingerprint test in a referene database with largely optimized errors. 2. Extration of Fingerprint's Biometri - Signature o build our database, we proeeded to the taking of fingerprints on different people; these fingerprint images were enhaned by a pretreatment proess to extrat the orresponding signature to eah fingerprint. hus, these signatures are stored in a referene database. 2.. Preproessing of Captured Fingerprints In this step, we proeeded the aquisition of fingerprints of different people with a sanner of type Ko-UF dpi, performed in our laboratory. he resulting images are hene stored as graysale with the appropriate format. However these images often present a noise during the aquisition phase that require a adapted filtering, whih the goal is to strengthen the ontrast by eliminating redundant points [3]. hen, these images are binarized by thresholding proess

2 08 Mostafa Boutahri et al.: Adaptation of Rigid Registration Algorithm to the Fingerprints Identifiation that assoiates the number 0 to the pixel if its value is below a predetermined threshold value and the number if the pixel value is equal to or greater than this threshold value. his last, varies aording to the quality of the aptured image and was therefore set to a gray level equal to 60. o failitate its exploitation, the image must undergo a thinning treatment that onsist to obtain a shemati image of the fingerprint, in whih all the lines have the thikness of a pixel [4] [5]. he simulation results was arried out using Matlab software-r204a (Figure. ) Extration of Minutiae o extrat the fingerprint minutiae (bifurations and endings), the alulation method used allows to detet them aording to the neighborhood of eah pixel following the ontinuity or disontinuity of the different lines [6] [7]. One this step is ompleted, false minutiae are eliminated from the piture for finally have a two-dimensional minutiae map, ontaining the two values 0 or (where indiates the presene of the minutiae and 0 his absene) [8]. (Figure 2) Figure. Fingerprint pretreatment proess. Figure 2. Proess of minutiae extration Data validation he biometri signatures obtained are stored in a referene database. During the identifiation and authentiation phase, a biometri signature is extrated from the fingerprint, objet of reognition, and is ompared to the signatures of the referene database using the iterative algorithm of fingerprints rigid registration. 3. Registration of Fingerprints Images by ICP he ICP algorithm applied on fingerprints is based on the iterative mathing of minutiae of a test fingerprint with their nearest neighbors on the next referene fingerprint.

3 Amerian Journal of Software Engineering and Appliations 205; 4(6): Meanwhile, a least squares tehnique is used to estimate the transformation from these orrespondenes. Indeed, at the end of eah iteration, the algorithm provides a list of mathed points, and an estimate of the registration transformation, this transformation is used in the next iteration for updating the list of mathed points. hey used their towers to alulate a new estimate of the transformation. hese steps are repeated until onvergene of the algorithm. 3.. Modeling of the Problem he registration problem an be laid more formally by introduing the different notations used throughout this work and desribing the general priniple of the registration proedure (figure 3). Figure 3. he priniple of registration proess. Given two fingerprint images (wo maps of minutiae): { q/ i : N i q} { / j : N } Q= = ; he soure image to register; P= p = ; he referene Image. j p We onsider the rigid registration of a soure image Q: Ω R to a target image P: Ω R S C he domains subsets of 2 R. Ω and S Ω of the two images Q and Pare 3.2. ICP Algorithm Applied to the Fingerprints he proposed registration method is an iterative method ICP, whih takes as input two point louds of two-dimensional, and outputs an optimal estimate of the transformation between the two sets. herefore, the purpose of this algorithm is to find the parameters of the transformation (rotation and translation) whih aligns a test fingerprint Q to another as referenep. he ost funtion used is given by: 2 N C min = + Rt,,j i= q ( Rq t i ) p j ( R) RR. = R. R= I2 det = j= : Np With R and t are the parameters of the transformation (rotation and translation respetively). he ICP algorithm reahes its speed and preision performane by iterating the following two steps:. First, we establish the orrespondenes linking the two sets of points: C () i = arg min ( i ) k k k j= : Np 2 R q + t p i= : N j q 2. hen, we alulate the new rotation and translation minimizing the least square riterion: N 2 = + k R, t i= q ( R, ) arg min k k Rq t p i () i Note that at eah iteration, ICP method uses the results obtained at the last iteration to yield a better result. hat is until the result onverges or that we have exeeded the maximum number of iterations alloated to the algorithm[9] Mathing Step In first step of the algorithm, the mathing is done by a simple algorithm to searh neighborhood, whih onsist to pass on all the points Np of P and to see if it s nearer or not that one of the already seleted lose neighbors, and if so, insert it. We then obtain a linear alulation time in the size of P. his method is alled linear searh or sequential searh. However, this method suffers from a problem of slowness if the set P is too large, then it is extremely expensive to test Np points in spae. Fortunately, in our ase of fingerprints, the number of mathing minutiae points does not exeed a few

4 0 Mostafa Boutahri et al.: Adaptation of Rigid Registration Algorithm to the Fingerprints Identifiation dozen. his searh algorithm will be used as referene to ompare other algorithms that use more omplex data storage strutures like Delaunay triangulation Matlab [0][], Kd-tree [2], et Delaunay triangulation invented by the Russian mathematiian Boris Delaunay in 934. his triangulation is applied for eah of the two sets (soure and target), then they are merged afterwards. he Kd-ree introdued in 975, the priniple is to try to plae eah point of the soure image in the tree, and up until one gets to the root of the tree while searhing losest neighbors. Still in the same step, looking for the nearest point is an important part if you want to get a method, whih is robust as well as effiient. his is why the detetion of the existene of aberrant points (outliers) is signifiant. he existene of outliers in point sets to math is very detrimental to our algorithm beause when we seek the transformation between the two sets, we assume that we have the same loud of points at different positions, assumption that's violated in presene of outliers. o solve the problem of outliers, we sort in asending order all pairings by omparing the distanes between paired points. hen we selet the n = r * n first pairings with r is a r parameter alled overlay oeffiient inluding in the interval r, r, and obtained by minimizing the funtion: [ ] min max Suh as ψ () r q e(r) = ; (See figure 4) + r λ nr k+ () ( d i ) e r 2 k =, d + = Rq + t p i k i k () i k+ n r i= λ= 2, and typially ( r min = 0.4 and r = ) max approah of the ICP method without outliers. he other points are dismissed as outliers ransformation Update In the seond step of the algorithm, the used approah is that of the singular value deomposition (SVD) applied in 2D. he idea is this:. Calulate the enter of mass p and q of the two point louds{ p i } and{ q i } : nr P = p and Q i n r i= nr 2. Construt the ovariane matries: = qi ( i=... n) r n r i= { i } { i} { i } { i} ( i =... n r) P = p p = p and Q = q q = q ' ' ' ' 3. Calulate the matrix 2*2 K R suh as ' ' ( 2* nr P, Q R ) ' ' K= PQ. 4. Write the matrix K as K= USV.., this deomposition is done by the SVD funtion predefined in Matlab by writing [ U. S. V ] = SVD ( K ) where U and V are orthogonal matries of orders 2 and S a diagonal matrix of size 2 * 2 whose diagonal ontains the singular values of K. 5. he parameters of the transformation are dedued by: ( ). ( ) R= UV. Rotation = p Rq ranslation Note that if det( R ) < 0 R= V* B* U where 0 B= 0 det( V.U) 3.3. Evaluation of the Method on Real Data of Fingerprints (Golden Setion Method) [3] Figure 4. Golden Setion Method. hese pairings will be those that we will use to estimate the transformation between the two louds following the We tested our approah by taking an inorporated referene database of 20 fingerprints taken from different people and another database for test ontaining 0 fingerprints, whih six are taken from people already on the database with different positions of those arried out beforehand, whereas the remaining four are removed from people not inluded in database. (Figure 5) Preproessing stage: For simpliity, we limit ourselves to suh minutiae endings. After pretreatment and extration of minutiae, we learly observe the presene of outliers, this is where the parameter r is automatially updated with eah iteration. he figure 6 shows two samples of fingerprint images taken from the referene and test database respetively and

5 Amerian Journal of Software Engineering and Appliations 205; 4(6): 07-4 this, before and after the preproessing stage. Figure 5. (a) Referene database; (b) est database. Fig. 7. Extration of the two point louds without outliers. Putative point mathes Fig. 6. wo samples of fingerprints taken from referene and test database respetively. (a) Before pretreatment; (b) After pretreatment. Alignment step of fingerprint images: his stage inludes the mathing based on the naive method of searhing the nearest neighbor ited in (3.2.), then an intermediate step is to rejet false omparison points (outliers) to reah finally the alulation of the new transformation (rotation and translation) minimizing the ost funtion. (Figures 7, 8, 9 and 0) Fig. 8. Appariements between the two point sets of extrated minutiae. 4. Results and Disussions he onfrontation results of the test fingerprints (Fi /i=, 2 0) to those of the referene (FRj 2 /j=,2 20) by the studied method are provided in the following table (able ). Fi: est fingerprint number i where i=(,2,,0); 2 FRj: Referene fingerprint number j where j=(,2,,20).

6 2 Mostafa Boutahri et al.: Adaptation of Rigid Registration Algorithm to the Fingerprints Identifiation Before registration Registration by brutefore Fig. 9. Fingerprint images before and after registration by ICP. Fig. 0. Visualization of the two point louds before and after registration by ICP. able. Least squares error between the set of minutiae in the test and referene database. F F2 F3 F4 F5 F6 F7 F8 F9 F0 FR.84e2 2.24e e2 3.50e2 3.00e3 2.0e2 2.43e2.726e e 7.02e2 FR e2 7.27e.235e2 0.27e3 4.23e e 3.25e3 3.5e2 4.2e3 3.20e3 FR3 7.20e 4.378e2 0.70e 5.43e e2.628e e 2.720e e 5.200e FR e2.435e3.804e2.780e e.443e2.376e e e e2 FR5.405e e2.403e e 0.5e2 7.67e 3.450e e2.24e e3

7 Amerian Journal of Software Engineering and Appliations 205; 4(6): F F2 F3 F4 F5 F6 F7 F8 F9 F0 FR e2.023e e-4.073e2 5.0e e e4 8.25e2 7.25e e4 FR7.035e e 9.072e e 3.72e 6.420e 5.2e e 6.034e e2 FR8 2.72e 0.860e e e e0.03e3.750e e3 3.72e e2 FR e3.075e e 0.089e e2.92e 3.79e e e.067e3 FR0.750e e e e2 3.02e e e2 0.60e e3 3.77e2 FR 2.300e2.924e.043e2 2.03e e.204e3.072e2.083e e2 4.50e3 FR2.942e 5.872e e 9.325e.00e4 6.2e2 3.06e 5.642e.642e e2 FR e2.07e e e2 8.4e2 5.7e 7.94e2 3.44e2 9.00e2 3.0e3 FR4.77e e2.487e3.622e 2.80e2.092e2.037e e3.847e e2 FR e2.380e-3.253e3 4.28e2.276e3.380e3.253e e3 8.43e e2 FR e 8.64e e3.322e.097e2 7.3e e e e e2 FR e e 5.30e 4.623e 3.654e-2.045e2 5.30e3 0.5e3 3.0e e2 FR e 0.809e e e e 5.9e e2 3.09e2 7.26e2 7.00e FR e e 4.750e e e2 7.04e2 4.72e2 4.32e 5.327e e2 FR e 4.273e 8.30e 6.2e2 4.60e2.323e e2 0.60e3.972e.40e3 hese results demonstrate that people whose fingerprints F, F6 and F0 are not listed on the referene database beause the value of the Least squares error is in the range of 0 to 0 4. As to persons whose fingerprints F2, F4, F7, F8 and F9, they are identified in the referene database and orrespond to people whose fingerprints are respetively FR5, FR9, FR7, FR9 and FR6. Indeed, the errors stored vary in the range of 0-2 to 0-4. Still based on the table, we note that the fingerprint F3 orresponds well to the referene fingerprints FR6 and FR3 with respetive errors 6, and 2, his demonstrate that the fingerprints FR6 and FR3 are taken from the same person. Similarly for the test fingerprint F5 that mathes the person suffering the two prelevements FR2 and FR7 with respetive errors in the range of 0-2 to Conlusion hus, the work proposed in this paper is based on a purely geometri approah by implementing a system of identifiation or verifiation by rigid registration of fingerprints, with the main objetives proessing speed and eliminating subjetivity based in partiular on mathematial riteria to evaluate the results, this approah inludes a hain of operations ranging from preproessing fingerprints to rigid registration and based on the minutiae extrated. Indeed, we have hosen the minutiae type terminations as information to onsider for guiding the registration. hen, after formalization of the general problem of registration, a registration method by ICP algorithm was proposed. At the rate of its sensitivity to noise and outliers, this algorithm has been improved by the introdution of a parameter, whih ats on the seletion of pairings. he latter are used to estimate the transformation between the two fingerprint images following the approah of the ICP method without outliers. he results thus obtained in our study are ompelling and indiate the effetiveness of the method in the field of identifiation of persons based on their fingerprints. Referenes [] Lorène, La biométrie multimodale: stratégies de fusion de sores et mesures de dépendane virtuelle. hèse de dotorat de Al Mutaz M. Abdalla, Safaai Dress, Nazar Zaki, "Detetion of Masses in Digital Mammogram Using Seond Order Statistis and Artifiial Neural Network", International Journal of Computer Siene & Information ehnology (IJCSI), Vol 3, No 3, pp June 20. [2] Morizet, Reonnaissane biométrique par fusion multimodale du visage et de l iris, hèse de dotorat éléom [3] Doublet, Revenu, Olivier, Reonnaissane biométrique sans ontat de la main intégrant des informations de forme et de texture, Frane eleom [4] Salil Prabhakar, Anil K. Jain, Learning Fingerprint Minutiae Loation and ype, International Conferene on Pattern Reognition (ICPR), [5] Chaohong, Advaned feature algorithms for automati fingerprint reognition system, University of New Yorkatbuffalo [6] A. Chaari, S. Lelandais, M. B. Ahmed, Fae lassifiation sheme simplifying identifiation in biometri databases, ransations on Systems, Signals & Devies (SSD), Shaker-Verlag, sous presse, [7] LIU L., JIANG., YANG J., and al., Fingerprint Registration by Maximization of Mutual Information, IEEE ransations on image proessing,5(5), 00-0, [8] M. Boutahri, S. El Yamani, S. Zeriouh, A. Bouzid and A. Roukhe, Fingerprint Identifiation by Artifiial Neural Network, Journal of Physial Siene and Appliation (David publishing), pp Jun 204. [9] D. Maltoni, D. Maio, A.K. Jain, S. Prabhakar Handbook of Fingerprint Reognition Springer, New York, [0] E. M.Gross, D. Wagner, KD trees and Delaunay-based linear interpolation for funtion learning: a omparison to neural networks with error bakpropagation, pp Nov 996.

8 4 Mostafa Boutahri et al.: Adaptation of Rigid Registration Algorithm to the Fingerprints Identifiation [] Barber, C. B., Dobkin, D. P., Huhdanpaa, H., he quikhull algorithm for onvexhulls. ACM rans. Math. Software 22 (4), , 996. [3] D. Chetverikov, D. Stepanov, P. Krsek, Robust Eulidean alignment of 3D point sets: the trimmed iterative losest point algorithm, Vol 23, Number 3, pp , Marh [2] Nuhter, A., Lingemann, K., Hertzberg, J., Cahed k d tree searh for ICP algorithms. In: Pro. Sixth Internat. Conf. on 3-D Digital Imaging and Modeling(3DIM), pp , 2007.