Advertisement

Fingerprint Image Quality Assessment Based on Oriented Pattern Analysis

  • Raimundo Claudio da Silva VasconcelosEmail author
  • Helio PedriniEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

Decision based on fingerprint image quality is crucial for automatic fingerprint classification and recognition tasks. Quality is challenging due to a variety of noise types that may exist in an image. Researches have been conducted to propose suitable combination of techniques for assessing fingerprint quality, however, it is difficult to achieve a generic solution for different data sets. This work proposes a fingerprint image quality indicator based on directional information inherent in fingerprint ridges and evaluates a metric for quality assessment. Experimental results on Fingerprint Verification Competition (FVC) data sets demonstrate the usability of the proposed index.

Keywords

Fingerprint images Quality Directional information 

References

  1. 1.
    Alonso-Fernandez, F., et al.: A comparative study of fingerprint image-quality estimation methods. IEEE Trans. Inf. Forensics Secur. 2(4), 734–743 (2007)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Chen, T., Jiang, X., Yau, W.: Fingerprint image quality analysis. In: IEEE International Conference on Image Processing, pp. 1253–1256 (2004)Google Scholar
  4. 4.
    Chen, Y., Dass, S., Jain, A.: Fingerprint quality indices for predicting authentication performance. In: International Conference on Audio-and Video-Based Biometric Person Authentication, pp. 160–170 (2005)Google Scholar
  5. 5.
    Feng, J., Jain, A.K.: Fingerprint reconstruction: from minutiae to phase. IEEE Trans. Pattern Anal. Mach. Intell. 33, 209–223 (2011)CrossRefGoogle Scholar
  6. 6.
    Griaule Biometrics. Griaule Big Data Biometrics (2018). http://www.griaulebiometrics.com/new/
  7. 7.
    Jain, A.K., Chen, Y., Demirkus, M.: Pores and ridges: fingerprint matching using level 3 features. IEEE Trans. Pattern Anal. Mach. Intell. 29, 15–27 (2007)CrossRefGoogle Scholar
  8. 8.
    Kass, M., Witkin, A.: Analyzing oriented patterns. Comput. Vis. Graphics Image Process. 37(3), 362–385 (1987)CrossRefGoogle Scholar
  9. 9.
    Lee, S., Choi, H., Choi, K., Kim, J.: Fingerprint-quality index using gradient components. IEEE Trans. Inf. Forensics Secur. 3(4), 792–800 (2008)CrossRefGoogle Scholar
  10. 10.
    Lim, E., Jiang, X., Yau, W.: Fingerprint quality and validity analysis. In: IEEE International Conference on Image Processing, vol. 1, 469–472 (2002)Google Scholar
  11. 11.
    Lim, E., Toh, K.-A., Suganthan, P., Jiang, X., Yau, W.-Y.: Fingerprint image quality analysis. In: IEEE International Conference on Image Processing, pp. 1241–1244 (2004)Google Scholar
  12. 12.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer, London (2009).  https://doi.org/10.1007/978-1-84882-254-2CrossRefzbMATHGoogle Scholar
  13. 13.
    Oliveira, M.A., Leite, N.J.: A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images. Pattern Recogn. 41(1), 367–377 (2008)CrossRefGoogle Scholar
  14. 14.
    Olsen, M., Xu, H., Busch, C.: Gabor filters as candidate quality measure for NFIQ 2.0. In: 5th IAPR International Conference on Biometrics, pp. 158–163 (2012)Google Scholar
  15. 15.
    Olsen, M.A., Smida, V., Busch, C.: Fingerprint Quality Assessment Algorithms (2015). http://www.nislab.no, https://www.dasec.h-da.de
  16. 16.
    Cappelli, A.F.R., Ferrara, M., Maltoni, D.: Fingerprint verification competition 2006. Biometric Technol. Today 15(7–8), 7–9 (2007)CrossRefGoogle Scholar
  17. 17.
    Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation based feature extraction in fingerprint images. Pattern Recogn. 28, 1657–1672 (1995)CrossRefGoogle Scholar
  18. 18.
    Ross, A., Nadgir, R.: A thin-plate spline calibration model for fingerprint sensor interoperability. IEEE Trans. Knowl. Data Eng. 20, 1097–1110 (2008)CrossRefGoogle Scholar
  19. 19.
    Shen, L., Kot, A.C., Koo, W.M.: Quality measures of fingerprint images. In: 3rd International Conference on Audio- and Video-Based Biometric Person Authentication, pp. 266–271 (2001)Google Scholar
  20. 20.
    Yun, E.-K., Cho, S.-B.: Adaptive fingerprint enhancement with fingerprint image quality analysis. Image Vis. Comput. 24, 101–110 (2006)CrossRefGoogle Scholar
  21. 21.
    Zhou, J., Chen, F., Gu, J.: A novel algorithm for detecting singular points from fingerprint images. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1239–1250 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Federal Institute of BrasíliaTaguatingaBrazil
  2. 2.Institute of ComputingUniversity of CampinasCampinasBrazil

Personalised recommendations