Abstract
Although many algorithms have been proposed for orientation field estimation, the results are not so satisfactory and the computational cost is expensive. In this paper, a novel algorithm based on straight-line model of ridge is proposed for the orientation field estimation. The algorithm comprises four steps, preprocessing original fingerprint image, determining the primary and secondary ridges of fingerprint foreground block using the top semi-neighbor searching algorithm, estimating block direction based on straight-line model of such a primary ridge and correcting the spurious block directions. Experimental results show that it achieves satisfying estimation accuracy with low computational time expense.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Zhou, J., Gu, J.W.: Modeling orientation fields of fingerprints with rational complex functions. Pattern Recogn. 37(2), 389–391 (2004)
O’Gorman, L., Nickerson, J.V.: An approach to fingerprint filter design. Pattern Recogn. 22(1), 362–385 (1987)
Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recogn. 28(11), 1657–1672 (1995)
Bazen, A.M., Gerez, S.H.: 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)
Zhou, J., Gu, J.: A model-based method for the computation of fingerprints’ orientation field. IEEE Trans. Image Process. 13(6), 821–835 (2004)
Gu, J., Zhou, J., Zhang, D.: A combination model for orientation field of fingerprints. Pattern Recogn. 37(3), 543–553 (2004)
Li, J., Yau, W.Y., Wang, H.: Constrained nonlinear models of fingerprint orientations with prediction. Pattern Recogn. 39(1), 102–114 (2006)
Nagaty, K.A.: On learning to estimate the block directional image of a fingerprint using a hierarchical neural network. Neural Networks 16(1), 133–144 (2003)
Ji, L.P., Yi, Z.: Fingerprint orientation field estimation using ridge projection. Pattern Recogn. 41(5), 1491–1503 (2008)
Nagaty, K.A.: Fingerprints classification using artificial neural networks: a combined structural and statistical approach. Neural Networks 14(9), 1293–1305 (2001)
Jain, A., Hong, L., Bolle, R.: On-line fingerprint verification. IEEE Trans. Pattern Anal. Mach. Intell. 19(4), 302–313 (1997)
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
Liu, H., Lv, X., Li, X., Liu, Y. (2010). Fingerprint Orientation Field Estimation: Model of Primary Ridge for Global Structure and Model of Secondary Ridge for Correction. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-12297-2_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12296-5
Online ISBN: 978-3-642-12297-2
eBook Packages: Computer ScienceComputer Science (R0)