Fingerprint Ridge Line Extraction Based on Tracing and Directional Feedback

  • Rui Ma
  • Yaxuan Qi
  • Changshui Zhang
  • Jiaxin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3801)


Fingerprint recognition and verification are always the key issues in intelligent technology and information security. Extraction of fingerprint ridge lines is a critical pre-processing step in fingerprint identification applications. Although existing algorithms for fingerprint extraction work well on good-quality images. Their performance decrease when handling poor-quality images. This paper addresses the ridge line extraction problem as curve tracking processes under the framework of probabilistic tracking. Each ridge line is modeled as sequential frames of a continuous curve and then traced by standard CONDENSATION algorithm in the area of computer vision. Additionally, local directional image is rectified with a feedback technique after each tracking step to improve the accuracy. The experimental results are compared with those obtained through existing well-known algorithms, such as local-binarization and sampling-tracing methods. In spite of greater computational complexity, the method proposed performs better both in terms of efficiency and robustness.


Gray Level Visual Tracking Poor Quality Image Fingerprint Image Intelligent Technology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Maio, D., Maltoni, D.: Direct Gray-scale Minutiae Detection in Fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 19, 27–40 (1997)CrossRefGoogle Scholar
  2. 2.
    Blake, A., Isard, M.: The CONDENSATION algorithm – conditional density propagation and applications to visual tracking. In: Advances in Neural Information Processing System, vol. 9, pp. 361–368 (1996)Google Scholar
  3. 3.
    Perez, P., Vermaak, J., Blake, A.: Data Fusion for Visual Tracking with Particles. Invited paper in proceedings of the IEEE 92, 495–513 (2004)Google Scholar
  4. 4.
    Ma, R., Qi, Y., et al.: A Novel Approach to Fingerprint Ridge Line Extraction. To appear in Proc. of International Symposium on Communications and Information Technologies (2005)Google Scholar
  5. 5.
    Domeniconi, C., Tari, S., Liang, P.: Direct Gray Scale Ridge Reconstruction in Fingerprint Images. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5, pp. 2941–2944 (1998)Google Scholar
  6. 6.
    Isard, M., Blake, A.: Contour Tracking by Stochastic Propagation of Conditional Density. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065, pp. 343–356. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  7. 7.
    Moayer, B., Fu, K.: A Tree System Approach for Fingerprint Pattern Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 8, 376–387 (1986)CrossRefGoogle Scholar
  8. 8.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Rui Ma
    • 1
  • Yaxuan Qi
    • 2
  • Changshui Zhang
    • 3
  • Jiaxin Wang
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Department of Computer ScienceTsinghua UniversityBeijingChina
  2. 2.Network Security Lab, Research Institute of Information TechnologiesTsinghua UniversityBeijingChina
  3. 3.State Key Laboratory of Intelligent Technology and Systems, Department of AutomationTsinghua UniversityBeijingChina

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