Abstract
Fingerprint recognition has been used from many years for identification of persons. However, conventional fingerprint recognition systems might fail with poor quality, noisy or rotated images. Recently, novel non-linear composite filters for correlation-based pattern recognition have been introduced. The filters are designed with information from distorted versions of reference object to achieve distortion-invariant recognition. Besides, a non-linear correlation operation is applied among the filter and the test image. These kinds of filters are robust to non-Gaussian noise. In this paper we apply non-linear composite filters for fingerprint verification. Computer simulations show performance of proposed filters with distorted fingerprints. In addition, in order to illustrate robustness to noise, filters were tested with noisy images.
Keywords
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, London (2009)
Jain, A.K., Prabhakar, S., Hong, L., Pankanti, S.: Filterbank-Based Fingerprint Matching. IEEE T. on Image Process. 9, 846–859 (2000)
Ross, A., Reisman, J., Jain, A.: Fingerprint Matching Using Feature Space Correlation. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol. 2359, pp. 48–57. Springer, Heidelberg (2002)
Cappelli, R., Maio, D., Maltoni, D., Nanni, L.: A Two-Stage Fingerprint Classification System. In: Workshop on Biometrics Methods and Applications, pp. 95–99. ACM, California (2003)
Venkataramani, K., Vijaya-Kumar, B.V.K.: Fingerprint Verification Using Correlation Filters. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 886–894. Springer, Heidelberg (2003)
VanderLugt, A.B.: Signal Detection by Complex Filtering. IEEE Trans. Inf. Theory. 10, 135–139 (1964)
Hester, C.F., Casasent, D.: Multivariant Technique for Multiclass Pattern Recognition. Appl. Opt. 19, 1758–1761 (1980)
Mahalanobis, A., Vijaya-Kumar, B.V.K., Casasent, D.: Minimum Average Correlation Energy Filters. Appl. Opt. 31, 1823–1833 (1987)
Refregier, P.: Filter Design for Optical Pattern Recognition: Multicriteria Optimization Approach. Optical Society of America 15, 854–856 (1990)
Maragos, P.: Morphological Correlation and Mean Absolute Error Criteria. In: Proc. Conf. IEEE Trans. Acoust. Speech Signal Process., pp. 1568–1571 (1989)
Kober, V., Alvarez-Borrego, J., Ovseyevich, I.A.: Adaptive Rank Order Correlations. Pattern Recognition and Image Analysis 14, 33–39 (2004)
Martínez-Díaz, S., Kober, V.: Nonlinear Synthetic Discriminant Function Filters for Illumination-Invariant Pattern Recognition. Opt. Eng. 47, 067201 (2008)
Fitch, J.P., Coyle, E.J., Gallagher Jr., N.C.: Median Filtering by Threshold Decomposition. IEEE Trans. Acoust. Speech Signal Process., 1183–1188 (1984)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez-Díaz, S., Carmona-Troyo, J.A. (2010). Fingerprint Verification with Non-linear Composite Correlation Filters. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Kittler, J. (eds) Advances in Pattern Recognition. MCPR 2010. Lecture Notes in Computer Science, vol 6256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15992-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-15992-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15991-6
Online ISBN: 978-3-642-15992-3
eBook Packages: Computer ScienceComputer Science (R0)