Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Fingerprint Registration Using Zernike Moments : An Approach for a Supervised Contactless Biometric System
Tahirou DJARA, Marc Kokou ASSOGBA, Amine NAIT-ALI, Antoine VIANOU
Pages - 254 - 271     |    Revised - 31-08-2015     |    Published - 30-09-2015
Volume - 9   Issue - 5    |    Publication Date - September / October 2015  Table of Contents
Contactless Biometry, Fingerprint, Zernike Moments, Image Registration.
In this work, we deal with contactless fingerprint biometrics. More specifically, we are interested in solving the problem of registration by taking into consideration some constraints such as finger rotation and translation. In the proposed method, the registration requires: (1) a segmentation technique to extract streaks, (2) a skeletonization technique to extract the center line streaks and (3) and landmarks extraction technique. The correspondence between the sets of control points, is obtained by calculating the descriptor vector of Zernike moments on a window of size RxR centered at each point. Comparison of correlation coefficients between the descriptor vectors of Zernike moments helps define the corresponding points. The estimation of parameters of the existing deformation between images is performed using RANSAC algorithm (Random SAmple Consensus) that suppresses wrong matches. Finally, performance evaluation is achieved on a set of fingerprint images where promising results are reported.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 S. Pankanti, S. Prabhakar, A. Jain, On the individuality of fingerprints, IEEE Trans. Pattern Anal. 24 (8) (2002) 1010–1025.
2 Biometric in governements post-9/11, National Science and Technology Council, http://www.biometrics.gov/Documents/ (2009) 71–73, [Oct. 20, 2014].
3 B. Zitova, J. Flusser, Image registration methods: a survey, Image and Vision Computing 21 (2003) 977–1000.
4 L. Liu, T. Jiang, J. Yang, al, Fingerprint registration by maximization of mutual information, IEEE Transactions on image processing 15 (5) (2006) 1100–1110.
5 K. Mali, S. Bhattacharya, Fingerprint recognition using global and local structures, International Journal on Computer Science and Engineering (IJCSE) 3 (1) (2011) 161–172.
6 R. Bahuguna, Fingerprint verification using hologram matched filterings, Psychological Review Presented at the 8th Meeting Biometric Consortium, San Jose, CA, Jun. 1996.
7 S. Gold, A. Rangarajan, A graduated assignment algorithm for graph matching, IEEE Trans. Pattern Anal. 18 (4) (1996) 377–388.
8 D. K. Isenor, S. G. Zaky, Fingerprint identification using graph matching, Pattern Recognit. 19 (2) (1986) 113–122.
9 Y. He, J. Tian, X. Luo, T. Zhang, Image enhancement and minutiae matching in fingerprint verification, Pattern Recognition Letters 24 (2003) 1349–1360.
10 C. Serief, Robust feature points extraction for image registration based on the nonsubsampled contourlet transform., International Journal of Electronics Communication. 63 (2) (2009) 148–152.
11 J. Sarvaiya, S. Patnaik, H. Goklani, Image registration using nsct and invariant moment, International Journal of Image Processing (IJIP) 4 (2) (2010) 119–130.
12 G. Parziale, E.-D. Santana, R. Hauke, The surround imager: A multi-camera touchless device to acquire 3d rolled-equivalent fingerprints, ICB, LNCS 3832 (2006) 244–250.
13 B. Hiew, A. Teoh, Y. Pang, Touch-less fingerprint recognition system, ICB, LNCS 3832 (2007) 24–29.
14 S. Mil’shtein, J. Palma, C. Liessner, M. Baier, A. Pillai, A. Shendye, Line scanner for biometric applications, IEEE Intern. Conf. on Technologies for Homeland Security (2008) 205–208.
15 R. D. Labati, A. Genovese, V. Piuri, F. Scotti, Measurement of the principal singular point in contact and contactless fingerprint images by using computational intelligence techniques, IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) (2010) 18–23.
16 C. Lee, S. Lee, J. Kim, S. J. Kim, Preprocessing of a fingerprint image captured with a mobile camera, ICB, LNCS 3832 (2006) 348–355.
17 T. Djara, M. K. Assogba, A. Naït-Ali, Caractérisation spatiale des empreintes de l’index en analyse biométrique, Actes du CARI, Yamoussoukro (2010) 501–508.
18 W. Osterburg, T. Parthasarathy, T. E. S. Raghavan, al, Developpement of a mathematical formula forthe calculation of fingerprint probabilities based on individual characteristics, Journal of the American Statistical Association 72 (360) (1977) 772–778.
19 C. Arcelli, G. S. di Baja, A width-independent fast thinning algorithm, IEEE Trans. Pattern Anal. Mach. Intell. (1985) 463–474.
20 N. Galy, Etude d’un systéme complet de reconnaissance d’empreintes digitales pour un capteur microsystéme á balayage, Institut National Polytechnique de Grenoble - INPG (Thése) (2005) 463–474.
21 R. J. Prokop, A. P. Reeves, A survey of moment-based techniques for unoccluded object representation and recognition, Computer Vision, Graphics, and Image Processing. Graphical Models and Image Processing 54 (5) (1992) 438–460.
22 R. Mukundan, K. R. Ramakrishnan, Moment Functions in Images Analysis. Theory and Applications, (1998), Ch. Zernike Moments, pp. 57–68.
23 R. Mukundan, K. R. Ramakrishnan, Fast computation of Legendre and Zernike moments, Pattern Recognition 28 (9) (1995) 1433–1442.
24 R. Mukundan, A contour integration method for the computation of zernike moments of a binary image, National Conference on Research and Development in Computer Science and its Applications (1997) 188–192.
25 W. Y. Kim, P. Yuan, A practical pattern recognition system for translation, scale, and rotation invariance, In Proceedings of the Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press (1994) 391–396.
26 L. V. Gool, T. Moons, D. Ungureanu, Affine/photometric invariants for planar intensity patterns, In Proceedings of the 4th European Conference on Computer Vision (1996) 642–651.
27 A. Khotanzad, Y. H. Hong, Invariant image recognition by zernike moments, IEEE Trans. Pattern Anal. Mach. Intell. 12 (5) (1990) 489–497.
28 P. A. Elsen, E. J. D. Pol, M. A. Viergever, Medical image matching a review with classification, IEEE Engineering in Medecine and Biology (1993) 26–39.
29 M. A. Fischler, R. C. Bolles, Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, 24(6) (1981) 381–395.
30 T. Kim, W. Yu, Performance evaluation of ransac family, In Proceedings of the British Machine Vision Conference (BMVC). (2009) 1–12.
31 Q. Zhao, L. Zhang, D. Zhang, N. Luo, Direct pore matching for fingerprint recognition, M. Tistarelli and M.S. Nixon (Eds.), ICB (2009) 597–606.
32 F. Liu, Q. Zhao, L. Zhang, D. Zhang, Fingerprint pore matching based on sparse representation, Pattern Recognition (ICPR) (2010) 1630–1633.
33 R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, 2nd Edition, (2004), Ch. Estimation - 2D Projective Transformations, pp. 117–121.
34 T. Svoboda, Ransac random sample consensus, http://cmp.felk.cvut.cz (2008) 1–18.
35 Lowe, David G. "Distinctive Image Features from Scale-Invariant Keypoints". International Journal of Computer Vision 60 (2): 91–110. (2004).. doi:10.1023/B:VISI.0000029664.99615.94.
36 Stephan Saalfeld. "Feature Extraction SIFT/MOPS (Fiji)". (2008). http://fiji.sc/Feature_Extraction.
Dr. Tahirou DJARA
Ecole Polytechnique d'Abomey-Calavi(EPAC) - Benin
Associate Professor Marc Kokou ASSOGBA
Ecole Polytechnique d'Abomey-Calavi(EPAC) - Benin
Professor Amine NAIT-ALI
Université Paris Est-Créteil - France
Professor Antoine VIANOU
Ecole Polytechnique d'Abomey-Calavi(EPAC) - Benin