Advertisement

Image Enhancement for Fingerprint Recognition Using Otsu’s Method

  • Puja S. PrasadEmail author
  • B. Sunitha Devi
  • Rony Preetam
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 500)

Abstract

The internal surfaces of human hands and feet of have minute ridges with furrows between each ridge. Fingerprints have very distinctive features and have been used over a long period of time for the identification of individuals and are now considered to be a very good authentication system for biometric identification. For successful authentication of fingerprint, features must be extracted properly. The different types of fingerprint enhancement algorithms used in image processing all provide different performance results depending on external and internal conditions. External conditions include types of sensors and pressure applied by the subject etc. Internal conditions include the body temperature of a subject and skin quality etc. In this paper, we enhance an image using Otsu’s method, which is one of the segmentation steps of image processing. This algorithm can improve the clarity of ridges and furrows of a fingerprint and enhances performance by reducing the total time for extraction of minutiae compare to other algorithms.

Keywords

Minutiae Gabor filtering Ridge ending Ridge bifurcation Wavelet domain Otsu’s method 

References

  1. 1.
    Handbook of Fingerprint Recognition by David Maltoni (Editor), Dario Maio, Anil K. Jain, Salil PrabhakarGoogle Scholar
  2. 2.
    Hong L, Wan Y, Jain AK (1998) Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell 20(8):777–789CrossRefGoogle Scholar
  3. 3.
    Raju Sonavane, Sawant BS (2007) Noisy fingerprint image enhancement technique for image analysis: a structure similarity measure approach. J Comput Sci Net Secur 7(9):225–230Google Scholar
  4. 4.
    Kukula EP, Blomeke CR, Modi SK, Elliott SJ (2008) Effect of human interaction on fingerprint matching performance, image quality, and minutiae count. International Conference on Information Technology and Applications, pp 771–776Google Scholar
  5. 5.
    Hsieh CT, Shyu SR, Hu CS (2005) An effective method of fingerprint classification combined with AFIS. EUC 2005: Conference Paper, Embedded and Ubiquitous Computing – EUC pp 1107–1122Google Scholar
  6. 6.
    Hong L, Wan Y, Jain A, Fingerprint image enhancement: algorithm and performance evaluation. East Lansing, MichiganGoogle Scholar
  7. 7.
    Fingerprint Minutiae Extraction, Department of Computer Science National Tsing Hua University Hsinchu, Taiwan 30043Google Scholar
  8. 8.
    Hong L, Jain A, Pankanti S, Bolle R (1996) Fingerprint enhancement. Pattern Recognit 202–207Google Scholar
  9. 9.
    Jain AK, Hong L, Pantanki S, Bolle R (1997) An identity authentication system using fingerprints. Proc IEEE 85(9):1365–1388CrossRefGoogle Scholar
  10. 10.
    Garris MD, Watson CI, McCabe RM, Wilson L (2001) National institute of standards and technology fingerprint database, Nov 2001Google Scholar
  11. 11.
    Guo Z, Hall RW (1989) Parallel thinning with two-subiteration algorithms. Commun ACM 32(3):359–373MathSciNetCrossRefGoogle Scholar
  12. 12.
    Jain AK, Farrokhnia F (1991) Unsupervised texture segmentation using Gabor filters. Pattern Recogn 24(12):167–186CrossRefGoogle Scholar
  13. 13.
    Jain AK, Hong L, Bolle RM (1997) On-line fingerprint verification. IEEE Trans Pattern Anal Mach Intell 19(4):302–314CrossRefGoogle Scholar
  14. 14.
    Gunjan VK, Shaik F, Kashyap A, Kumar A (2017) An interactive computer aided system for detection and analysis of pulmonary TB. Helix J 7(5):2129–2132. ISSN 2319–5592Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Puja S. Prasad
    • 1
    Email author
  • B. Sunitha Devi
    • 1
  • Rony Preetam
    • 1
  1. 1.CMR Institute of TechnologyHyderabadIndia

Personalised recommendations