Abstract
We introduce a novel coarse ridge orientation smoothing algorithm based on orthogonal polynomials, which can be used to estimate the orientation field (OF) for fingerprint areas of no ridge information. This method does not need any base information of singular points (SPs). The algorithm uses a consecutive application of filtering- and model-based orientation smoothing methods. A Gaussian filter has been employed for the former. The latter conditionally employs one of the orthogonal polynomials such as Legendre and Chebyshev type I or II, based on the results obtained at the filtering-based stage. To evaluate our proposed method, a variety of exclusive fingerprint classification and minutiae-based matching experiments have been conducted on the fingerprint images of FVC2000 DB2, FVC2004 DB3 and DB4 databases. Results showed that our proposed method has achieved higher SP detection, classification, and verification performance as compared to competing methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bazen, A.M., Gerez, S.H., 2002. Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Trans. Pattern Anal. Mach. Intell., 24(7):905–919. [doi:10.1109/TPAMI.2002.1017618]
Cappelli, R., Lumini, A., Maio, D., Maltoni, D., 1999. Fingerprint classification by directional image partitioning. IEEE Trans. Pattern Anal. Mach. Intell., 21(5):402–421. [doi:10.1109/34.765653]
Ford, R.M., Strickland, R.N., 1995. Representing and visualizing fluid flow images and velocity data by nonlinear dynamical systems. Graph. Models Image Process., 57(6):462–482. [doi:10.1006/gmip.1995.1040]
Gu, J., Zhou, J., Zhang, D., 2004. A combination model for orientation field of fingerprints. Pattern Recogn., 37(3):543–553. [doi:10.1016/S0031-3203(03)00178-X]
Jain, A.K., Pankanti, S., 2000. Fingerprint Classification and Matching. In: Bovyk, A. (Ed.), Hand Book of Image and Video Processing (1st Ed.). Academic Press, London, p.821–835.
Jain, A.K., Hong, L., Pankanti, S., Bolle, R., 1997. An identityauthentication system using fingerprints. Proc. IEEE, 85(9):1365–1388. [doi:10.1109/5.628674]
Jiang, X.D., Yau, W.Y., 2000. Fingerprint Minutiae Matching Based on the Local and Global Structures. 15th Int. Conf. on Pattern Recognition, p.1042–1045.
Jilli, W., 2003. Fingerprint Verification MatlabGUI, Final Year Honors Project for CS Bachelor Degree. Available from http://www.comp.hkbu.edu.hk/~vincent/hp/yytang_9905 0056_program.zip
Kass, M., Witkin, A., 1987. Analyzing oriented patterns. Comput. Vis. Graph. Image Process., 37(3):362–385. [doi:10.1016/0734-189X(87)90043-0]
Li, J., Yau, W., Wang, H., 2006. Constrained nonlinear models of fingerprint orientations with prediction. Pattern Recogn., 39(1):102–114. [doi:10.1016/j.patcog.2005.08.010]
Maio, D., Jain, A.K., 2002. FVC2000: fingerprint verification competition. IEEE Trans. Pattern Anal. Mach. Intell., 24(3):402–412. [doi:10.1109/34.990140]
Maio, D., Maltoni, D., 1997. Direct gray-scale minutiae detection in fingerprints. IEEE Trans. Pattern Anal. Mach. Intell., 19(1):27–40. [doi:10.1109/34.566808]
Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K., 2004. FVC2004: third fingerprint verification competition. LNCS, 3072:1–7.
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S., 2009. Handbook of Fingerprint Recognition (2nd Ed.). Springer, New York. [doi:10.1007/978-1-84882-254-2]
Nocedal, J., Wright, S.J., 2006. Numerical Optimization (2nd Ed.). Springer, New York, USA.
Perko, L., 1991. Differential Equations and Dynamical Systems. Springer-Verlag, New York, USA.
Ram, S., Bischof, H., Birchbauer, J., 2008. Curvature Preserving Fingerprint Ridge Orientation Smoothing Using Legendre Polynomials. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.122–129.
Rao, A.R., Jain, R.C., 1992. Computerized flow field analysis: oriented texture fields. IEEE Trans. Pattern Anal. Mach. Intell., 14(7):693–709. [doi:10.1109/34.142908]
Rao, A.R., Schunck, B.G., 1989. Computing Oriented Texture Fields. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.61–68. [doi:10.1109/CVPR.1989.37829]
Sherlock, B.G., Monro, D.M., 1993. A model for interpreting fingerprint topology. Pattern Recogn., 26(7):1047–1993. [doi:10.1016/0031-3203(93)90006-I]
Stock, R.M., Swonger, C.W., 1969. Development and Evalutation of a Reader of Fingerprint Minutiae. Technical Report, XM-2478-X-1:13–17, Cornell Aeronautical Laboratory, New York, USA.
Tashk, A., Helfroush, M.S., Muhammadpour, M., 2009. Improvement of Fingerprint Orientation Estimation by a Modification of Fingerprint Orientation Model Based on 2D Fourier Expansion (M-FOMFE). 2nd Int. Conf. on Computer Control and Communication, p.1–6. [doi:10.1109/IC4.2009.4909232]
Vizcaya, P.R., Gerhardt, L.A., 1996. A nonlinear orientation model for global description of fingerprints. Pattern Recogn., 29(7):1221–1231. [doi:10.1016/0031-3203(95)00154-9]
Wang, Y., Hu, J., Phillips, D., 2007. A fingerprint orientation model based on 2D Fourier expansion (FOMFE) and its application to singular-point detection and fingerprint indexing. IEEE Trans. Pattern Anal. Mach. Intell., 29(4):573–585. [doi:10.1109/TPAMI.2007.1003]
Zhou, J., Gu, J., 2004a. A model-based method for the computation of fingerprints orientation field. IEEE Trans. Image Process., 13(6):821–835. [doi:10.1109/TIP.2003.822608]
Zhou, J., Gu, J., 2004b. Modeling orientation fields of fingerprints with rational complex functions. Pattern Recogn., 37(2):389–391. [doi:10.1016/S0031-3203(03)00186-9]
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tashk, A., Helfroush, M.S. & Dehghani, M.J. A Chebyshev/Legendre polynomial interpolation approach for fingerprint orientation estimation smoothing and prediction. J. Zhejiang Univ. - Sci. C 11, 976–988 (2010). https://doi.org/10.1631/jzus.C0910749
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.C0910749