Skip to main content
SpringerLink
Log in

Global Normalization for Fingerprint Image Enhancement

  • Conference paper
  • First Online:
Computational Vision and Bio-Inspired Computing ( ICCVBIC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1108))

Abstract

The enhancement in fingerprint image remains as a vital step to recognize or verify the identity of person. The noise is influenced during the acquisition of fingerprint image. The poor quality images are captured and leads to inaccurate levels of discrepancy in values of gray level beside the ridges and furrows because of non-uniformity of ink and contact of finger on scanner. This poor quality images are affect to the minutiae extraction algorithm which may extract incorrect minutiae and affect to the fingerprint matching during post-processing. Normalization is the pre-processing step for increase the quality of images by removing the noise and alters the range of pixel intensity values. The mean and variance are used in process to reduce variants in gray-level values along ridges and valleys. In this paper show the result of applied famous global normalization and prove by empirical analysis of an image that normalization is remain good for enhancement process and make the noise free image which is useful for next step of fingerprint recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Lee, H.C., Gaensslen, R.E.: Advances in Fingerprint Technology. CRC Press, Boca Raton (2001)

    Google Scholar 

  2. Ratha, N.K., Bolle, R.M.: Fingerprint image quality estimation. IBM Computer Science Research Report (1999)

    Google Scholar 

  3. Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1), 4–20 (2004)

    Article  Google Scholar 

  4. Biometrics: Technologies and Global Markets. Published: November 2010 Report Code: IFT042C. http://www.bccresearch.com/market-research/information-technology/biometrics-technologies-markets-ift042c.html. Accessed Apr 2015

  5. Biometrics: Technologies and Global Markets. Published: January 2016 Report Code: IFT042E. https://www.bccresearch.com/market-research/information-technology/biometrics-technologies-markets-report-ift042e.html. Accessed Oct 2017

  6. Maltoni, D., Maio, D., Jain, A.K.: Handbook of Fingerprint Recognition, 3rd edn. Springer, New York (2009)

    Book  Google Scholar 

  7. Carlson, D.: Biometrics - Your Body as a Key, 2 June 2014, http://www.dynotech.com/articles/biometrics.shtml

  8. Mario, D., Maltoni, D.: Direct gray-scale minutiae detection in fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 19(1), 27–40 (1997)

    Article  Google Scholar 

  9. Patel, M.B., Patel, R.B., Parikh, S.M., Patel, A.R.: An improved O’Gorman filter for fingerprint image enhancement. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 200–209. IEEE, August 2017

    Google Scholar 

  10. Patel, M.B., Parikh, S.M., Patel, A.R.: Performance improvement in preprocessing phase of fingerprint recognition. In: Satapathy, S., Joshi, A. (eds.) Information and Communication Technology for Intelligent Systems, pp. 521–530. Springer, Singapore (2019)

    Chapter  Google Scholar 

  11. Patel, M.B., Parikh, S.M., Patel, A.R.: An improved approach in fingerprint recognition algorithm. In: Published in International Conference on Computational Strategies for Next Generation Technologies (NEXTCOM 2017), Organized by CT Institute of Engineering Management & Technology Shahpur Jalandhar, 25–26 November 2017. Proceeding in Springer CCIS Series (2017). ISSN No. – 1865-0929

    Google Scholar 

  12. Patel, M., Parikh, S.M., Patel, A.R.: An improved approach in core point detection algorithm for fingerprint recognition. In: 3rd International Conference on Internet of Things and Connected Technologies. Elsevier (2018)

    Google Scholar 

  13. Kim, B.G., Kim, H.J., Park, D.J.: New enhancement algorithm for fingerprint images. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 3, pp. 879–882. IEEE (2002)

    Google Scholar 

  14. Shi, Z., Govindaraju, V.: A chaincode based scheme for fingerprint feature extraction. Pattern Recogn. Lett. 27(5), 462–468 (2006)

    Article  Google Scholar 

  15. Kocevar, M., Kacic, Z.: Efficiency analysis of compared normalization methods for fingerprint image enhancement. ARPN J. Syst. Softw. 3(3), 40–45 (2013)

    Google Scholar 

  16. Hong, L., Member, S., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)

    Article  Google Scholar 

  17. Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC2000: fingerprint verification competition. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 402–412 (2002)

    Article  Google Scholar 

  18. FVC2000 (n.d.). http://bias.csr.unibo.it/fvc2000/databases.asp. Accessed June–July 2018

  19. Bong, F.F.L.D.: FingerDOS: a fingerprint database based on optical sensor. WSEAS Trans. Inf. Sci. Appl. 12(29), 297–304 (2015)

    Google Scholar 

  20. Fingerprint Database Based on Optical Sensor (FingerDOS) (n.d.). https://fingerdos.wordpress.com/. Accessed June–July 2018

Download references

Author information

Authors and Affiliations

  1. A.M.Patel Institute of Computer Studies, Ganpat University, Gujarat, India

    Meghna B. Patel & Satyen M. Parikh

  2. Florida Polytechnic University, Lakeland, FL, USA

    Ashok R. Patel

Authors
  1. Meghna B. Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Satyen M. Parikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashok R. Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meghna B. Patel .

Editor information

Editors and Affiliations

  1. Department of CSE, RVS Technical Campus, Coimbatore, India

    S. Smys

  2. Faculty of Engineering, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. School of Computing, Tokyo Institute of Technology, Tokyo, Japan

    Abdullah M. Iliyasu

Ethics declarations

✓ All authors declare that there is no conflict of interest.

✓ No humans/animals involved in this research work.

✓ We have used our own data.

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Patel, M.B., Parikh, S.M., Patel, A.R. (2020). Global Normalization for Fingerprint Image Enhancement. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_111

Download citation

Publish with us

Policies and ethics

Search

Navigation