Abstract
A new theoretical approach to construction of efficient algorithms for fingerprint image enhancement is proposed. The approach comprises novel modifications of advanced orientation field estimation techniques such as the method of fingerprint core extraction based on Poincaré indexes and model-based smoothing for the gradient-based approximation of an orientation field by Legendre polynomials, and new adaptive Gabor filtering technique based on holomorphic transformations of coordinates.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Which can be found in fingerprint images.
- 2.
Of the fingerprint to be analyzed.
References
FVC ongoing. https://biolab.csr.unibo.it/fvcongoing/UI/Form/Home.aspx. 20 March 2015
Bazen, A., Gerez, S.: Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 905–919 (2002)
Bhanu, B., Tan, X.: Computational Algorithms for Fingerprint Recognition (Kluwer International Series on Biometrics, 1). Kluwer Academic Publishers, Norwell (2003)
Cohn, H.: Conformal Mapping on Riemann Surfaces. Dover London, New York (1967)
Dremin, A., Khachay, M.Y., Leshko, A.: Fingerprint identification algorithm based on delaunay triangulation and cylinder codes. In: AIST 2014, pp. 128–139 (2014)
Feng, J., Zhou, J., Jain, A.: Orientation field estimation for latent fingerprint enhancement. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 925–940 (2013)
Gottschlich, C.: Curved-region-based ridge frequency estimation and curved gabor filters for fingerprint image enhancement. IEEE Trans. Image Process. 21(4), 2220–2227 (2012)
Gu, J., Zhou, J., Zhang, D.: A combination model for orientation field of fingerprints. Pattern Recogn. 37(3), 543–553 (2004)
Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)
Jiang, X., Yau, W.Y., Wee, S.: Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge. Pattern Recogn. 34(5), 999–1013 (2001)
Karimimehr, N., Shirazi, A., Keshavars Bahaghighat, M.: Fingerprint image enhancement using gabor wavelet transform. In: 2010 18th Iranian Conference on Electrical Engineering (ICEE), pp. 316–320, May 2010
Kass, M., Witkin, A.: Analyzing oriented patterns. Comput. Vis. Graph. Image Process. 37(3), 362–385 (1987)
Kawagoe, M., Tojo, A.: Fingerprint pattern classification. Pattern Recogn. 17(3), 295–303 (1984)
Khachai, M.Y., Leshko, A.S., Dremin, A.V.: The problem of fingerprint identification: a reference database indexing method based on delaunay triangulation. Pattern Recogn. Image Anal. 24(2), 297–303 (2014)
Liu, M., Chen, X., Wang, X.: Latent fingerprint enhancement via multi-scale patch based sparse representation. IEEE Trans. Inf. Forensics Secur. 10(1), 6–15 (2015)
Maltoni, D., Cappelli, R.: Advances in fingerprint modeling. Image Vis. Comput. 27(3), 258–268 (2009)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer Publishing Company, Incorporated, London (2009)
Mihăilescu, P., Mieloch, K., Munk, A.: Fingerprint classification using entropy sensitive tracing. In: Bonilla, L.L., Moscoso, M., Platero, G., Vega, J.M. (eds.) Progress in Industrial Mathematics at ECMI 2006, pp. 928–932. Springer, Heidelberg (2008)
Oliveira, M., Leite, N.: A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images. Pattern Recogn. 41(1), 367–377 (2008)
Poincaré, H.: Mémoire sur les courbes définies par une équation différentielle. Journal de mathématiques pures et appliquées 7, 375–422 (1881)
Ram, S., Bischof, H., Birchbauer, J.: Modelling fingerprint ridge orientation using Legendre polynomials. Pattern Recogn. 43(1), 342–357 (2010)
Sherlock, B., Monro, D.: A model for interpreting fingerprint topology. Pattern Recogn. 26(7), 1047–1055 (1993)
Suetin, P.: Orthogonal Polynomials in Two Variables. Gordon and Breach, Amsterdam (1999)
Turroni, F., Cappelli, R., Maltoni, D.: Fingerprint enhancement using contextual iterative filtering. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 152–157 (2012)
Voss, H.U., Timmer, J., Kurths, J.: Nonlinear dynamical system identification from uncertain and indirect measurements. Int. J. Bifurcat. Chaos 14, 1905–1933 (2004)
Wang, W., Li, J., Huang, F., Feng, H.: Design and implementation of log-gabor filter in fingerprint image enhancement. Pattern Recogn. Lett. 29(3), 301–308 (2008)
Zhou, J., Gu, J.: Modeling orientation fields of fingerprints with rational complex functions. Pattern Recogn. 37(2), 389–391 (2004)
Acknowledgements
This research was supported by Russian Science Foundation, grant no. 14-11-00109.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Khachay, M.Y., Pasynkov, M. (2015). Theoretical Approach to Developing Efficient Algorithms of Fingerprint Enhancement. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-26123-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26122-5
Online ISBN: 978-3-319-26123-2
eBook Packages: Computer ScienceComputer Science (R0)