Advertisement

Biometric Image Enhancement, Feature Extraction and Recognition Comprising FFT and Gabor Filtering

  • Al BashirEmail author
  • Mehnaz Tabassum
  • Niamatullah Naeem
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)

Abstract

Biometrics is a technology used to identify, analyze, and measure an individual’s physical and behavioral characteristics. It is used for authenticating and authorizing a person. Among all other biometric authentication, fingerprint recognition is the most known and used solution to authenticate people on biometric systems. Usually, fingerprint recognition approaches are minutiae-based and correlation-based. However, the minutiae-based approach is popular and extensively used method for fingerprint authentication, it shows poor performance for low quality images and insecure over data-passing channel. In proposed methodology, feature-based approach for fingerprint recognition is developed by enhancing the low quality image using FFT and Gaussian filter and by extracting the feature of the image using Gabor filter. Similarity measurement is done by calculating the cosine-similarity value of correlation factors. The cosine-similarity value of correlation factors of input image and template image are computed and compared. If it is over a certain threshold the result of the matching process is positive otherwise negative.

Keywords

Biometrics Fingerprint recognition FFT Gaussian filter Gabor filter 

References

  1. 1.
    Iqbal, A.A.: An overview of leading biometrics technologies used for human identity. In: Student Conference on Engineering Sciences and Technology, SCONEST 2005. IEEE (2005)Google Scholar
  2. 2.
    Haghighat, M., Zonouz, S., Abdel-Mottaleb, M.: CloudID: trustworthy cloud-based and cross-enterprise biometric identification. Expert Syst. Appl. 42(21), 7905–7916 (2015)CrossRefGoogle Scholar
  3. 3.
    Tuyls, P., et al.: Practical biometric authentication with template protection. In: AVBPA, vol. 3546 (2005)Google Scholar
  4. 4.
    Erkin, Z., et al.: Privacy-preserving face recognition. In: International Symposium on Privacy Enhancing Technologies Symposium. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Osadchy, M., et al.: Scifi-a system for secure face identification. In: 2010 IEEE Symposium on Security and Privacy (SP). IEEE (2010)Google Scholar
  6. 6.
    Garg, B., et al.: Fingerprint recognition using Gabor filter. In: 2014 International Conference on Computing for Sustainable Global Development (INDIACom). IEEE (2014)Google Scholar
  7. 7.
    Maltoni, D., et al.: Handbook of Fingerprint Recognition. Springer, London (2009)CrossRefGoogle Scholar
  8. 8.
    Charikar, M.S.: Similarity estimation techniques from rounding algorithms. In: Proceedings of the Thirty-Fourth Annual ACM Symposium on Theory of Computing. ACM (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJagannath UniversityDhakaBangladesh

Personalised recommendations