Abstract
As one key step of the automatic fingerprint identification system (AFIS), fingerprint image segmentation can decrease the affection of the noises in the background region and handing time of the subsequence algorithms and improve the performance of the AFIS. Markov Chain Monte Carlo (MCMC) method has been applied to medicine image segmentation for decade years. This paper introduces the MCMC method into fingerprint image segmentation and brings forward the fingerprint image segmentation algorithm based on MCMC. Firstly, it generates a random sequence of closed curves as Markov Chain, which is regarded as the boundary between the fingerprint image region and the background image region and uses the boundary curve probability density function (BCPDF) as the index of convergence. Then, it is simulated by Monte Carlo method with BCPDF as parameter, which is converged to the maximum. Lastly, the closed curve whose BCPDF value is maximal is regarded as the ideal boundary curve. The experimental results indicate that the method is robust to the low-quality finger images.
Supported by the National Natural Science Foundation of China under Grant No. 06403010, Shandong Province Science Foundation of China under Grant No.Z2004G05 and Anhui Province Education Department Science Foundation of China under Grant No.2005KJ089.
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
Jain, A.K., Uludag, U., Hsu, R.L.: Hiding a Face in a Fingerprint Image. In: Proc. ICPR, Que-bec City, pp. 756–759 (2002)
Zhan, X.S.: Research on Several key issues related to AFIS Based on verification mode. Ph.D Dissertation, Najing University (2003)
Jain, A.K., Hong, L., Bolle, R.: On-line fingerprint verification. IEEE Transactions on Pat-tern Analysis and Machine Intelligence, 302–314 (1997)
Mehtre, B.M., Murthy, N.N., Kapoor, S., Chatterjee, B.: Segmentation of fingerprint im-ages using the directional images. Pattern Recognition, 429–435 (1987)
Mehtre, B.M., Chatterjee, B.: Segmentation of fingerprint images-a composite method. Pattern Recognition, 1657–1672 (1995)
Chen, X., Tian, J., Cheng, J., Yang, X.: Segmentation of Fingerprint Images Using Linear Classifier. EURASIP Journal on Applied Signal Processing, 480–494 (2004)
Bazen, A.M., Gerez, S.H.: Segmentation of Fingerprint Images. In: Proc. Pro RISC 2000, 12th Annual Workshop on Circuits, Systems and Signal Processing, Veldhoven, The Netherlands, (November 29-30 2001)
Yin, Y.L., Yang, X.K., Chen, X., Wang, H.Y.: Method Based on Quadric Surface Model for Fingerprint Image Segmentation, Defense and Security. In: Proceedings of SPIE, pp. 417–324 (2004)
Green, P.J.: Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination. Biometrika, 711–732 (1995)
Zhu, S.C., Zhang, R., Tu, Z.W.: Integrating Bottom- Up/ Top- Down for Object Recog-nition by Data Driven Markov Chain Monte Carlo. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 738–745. Hilton Head Island, USA (2000)
Tu, Z.W., Zhu, S.C., Shum, H.Y.: Image Segmentation by Data Driven Markov Chain Monte Carlo. In: Proc, ICCV 2001. Eighth IEEE International Conference on Computer Vi-sion, Canada, Vancouver, pp. 131–138 (2001)
Tu, Z.W., Zhu, S.C.: Parsing Images into Region and Curve Processes [EB/OL] (2002), http://www.stat.ucla.edu/ztu/DDMCMC/curves/region-curve.htm
He, Y.L., Tian, J., Zhang, X.P.: Fingerprint Segmentation Method Based on Markov Ran-dom Field. In: Proceedings of the 4th China Graph Conference, pp. 149–156 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhan, X., Sun, Z., Yin, Y., Chen, Y. (2005). A Method Based on the Markov Chain Monte Carlo for Fingerprint Image Segmentation. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_28
Download citation
DOI: https://doi.org/10.1007/11540007_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
eBook Packages: Computer ScienceComputer Science (R0)