Advertisement

Orientation Field Estimation with Local Information and Total Variation Regularization for Incomplete Fingerprint Image

  • Xiumei Cai
  • Hao Xu
  • Jinlu Ma
  • Wei Peng
  • Haoyang Shi
  • Shaojie Tang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

Orientation field (OF) estimation is an important procedure in fingerprint image preprocessing. As for the hard problem that traditional methods cannot estimate the OF on incomplete fingerprint image accurately and the subsequent recognition will be influenced unavoidably, we propose an algorithm for the OF estimation which combines together the fidelity term of the local information from incomplete fingerprint image and a total variation (TV) regularization term. The local information involves the OFs evaluated by the traditional gradient-based method and the zero-pole model-based method. The experimental results demonstrate that proposed algorithm is effective in reconstructing the OF of incomplete fingerprint image.

Keywords

Incomplete fingerprint Orientation Field Total variation Regularization Estimation 

Notes

Acknowledgments

This work was supported in part by the project for the innovation and entrepreneurship in Xi’an University of Posts and Telecommunications (2018SC-03), the Key Lab of Computer Networks and Information Integration (Southeastern University), Ministry of Education, China (K93-9-2017-03), the Department of Education Shaanxi Province (16JK1712), Shaanxi Provincial Natural Science Foundation of China (2016JM8034, 2017JM6107), and the National Natural Science Foundation of China (61671377, 51709228).

References

  1. 1.
    Yilong, Y., Xinbao, N., Xiaomei, Z.: Development and application of automatic fingerprint identification technology. J.-Nanjing Univ. Nat. Sci. Ed. 38(1), 29–35 (2002).  https://doi.org/10.3321/j.issn:0469-5097.2002.01.005CrossRefGoogle Scholar
  2. 2.
    Kass, M., Witkin, A.: Analyzing oriented patterns. Read. Comput. Vis. 268–276 (1987).  https://doi.org/10.1016/b978-0-08-051581-6.50031-3Google Scholar
  3. 3.
    Wang, Y., Jiankun, H., Han, F.: Enhanced gradient-based algorithm for the estimation of fingerprint orientation fields. Appl. Math. Comput. 185(2), 823–833 (2007).  https://doi.org/10.1016/j.amc.2006.06.082CrossRefzbMATHGoogle Scholar
  4. 4.
    Feng, J., Jain, A.K.: Fingerprint reconstruction: from minutiae to phase. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 209–223 (2011).  https://doi.org/10.1109/TPAMI.2010.77CrossRefGoogle Scholar
  5. 5.
    Sherlock, B.G., Monro, D.M.: A model for interpreting fingerprint topology. Pattern Recognit. 26(7), 1047–1055 (1993).  https://doi.org/10.1016/0031-3203(93)90006-ICrossRefGoogle Scholar
  6. 6.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60(1-4), 259–268 (1992).  https://doi.org/10.1016/0167-2789(92)90242-FMathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Maio, D., et al.: FVC2004: Third fingerprint verification competition. In: Biometric Authentication, pp. 1–7. Springer, Heidelberg (2004)  https://doi.org/10.1007/978-3-540-25948-0_1Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xiumei Cai
    • 1
  • Hao Xu
    • 1
  • Jinlu Ma
    • 1
  • Wei Peng
    • 1
  • Haoyang Shi
    • 1
  • Shaojie Tang
    • 1
  1. 1.School of AutomationXi’an University of Posts and TelecommunicationsXi’anChina

Personalised recommendations