Advertisement

Variant of Nearest Neighborhood Fingerprint Storage System by Reducing Redundancies

  • K. Anjana
  • K. PraveenEmail author
  • P. P. Amritha
  • M. Sethumadhavan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 910)

Abstract

Biometric security is really important when it is the case of proving a individual’s identity. Fingerprint, iris, face, and gesture are the main biometric technologies. Fingerprint is the most convenient biometric which is used for proving an individual’s identity. Minutiae are said to be the unique representation of a fingerprint. There are different schemes in the literature for efficient storage of minutiae. Recently, a binary tree-based approach for efficient minutiae storage was proposed in the literature by removing the redundancies. We found out that the existence of redundancy in nearest neighborhood method reduces the efficiency. In this paper, we propose nearest neighborhood method by reducing redundancies for better efficiency. Comparative study of these proposed systems with existing scheme is done. As a result, we found out that, even though the complexity of algorithm is high, storage will be efficient.

References

  1. 1.
    Abutaleb, A.S., Kamel, M.: A genetic algorithm for the estimation of ridges in fingerprints. IEEE Trans. Image Process. 8(8), 1134–1139 (1999)CrossRefGoogle Scholar
  2. 2.
    Alibeigi, E., Rizi, M.T., Behnamfar, P.: Pipelined minutiae extraction from fingerprint images. In: Canadian Conference on Electrical and Computer Engineering, 2009. CCECE’09, pp. 239–242. IEEE, May 2009Google Scholar
  3. 3.
    Anjana, K., Praveen, K., Amritha, P.P.: Binary tree based fingerprint representation along with feature bit. IJPAM 118(20), 3751–3760 (2018)Google Scholar
  4. 4.
    Ceguerra, A.V., Koprinska, I.: Integrating local and global features in automatic fingerprint verification. In: 2002 Proceedings of 16th International Conference on Pattern Recognition, vol. 3, pp. 347–350. IEEE (2002)Google Scholar
  5. 5.
    Donahue, M.J., Rokhlin, S.I.: On the use of level curves in image analysis. CVGIP: Image Underst. 57(2), 185–203 (1993)CrossRefGoogle Scholar
  6. 6.
    Feng, J., Jain, A.K.: Fingerprint reconstruction: from minutiae to phase. IEEE Trans. Pattern Analy. Mach. Intell. 33(2), 209–223 (2011)CrossRefGoogle Scholar
  7. 7.
    Galton, F.: Finger Prints. Macmillan and Company, New York (1892)Google Scholar
  8. 8.
    Gamassi, M., Piuri, V., Scotti, F.: Fingerprint local analysis for high-performance minutiae extraction. In: 2005 International Conference on Image Processing, ICIP 2005, vol. 3, pp. III-265. IEEE, Sept 2005Google Scholar
  9. 9.
    Henry, E.: Classification and Uses of Finger Prints. [Sl], George Routledge and Sons (1900)Google Scholar
  10. 10.
    Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)CrossRefGoogle Scholar
  11. 11.
    Jain, A.K., Prabhakar, S., Hong, L.: A multichannel approach to fingerprint classification. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 348–359 (1999)CrossRefGoogle Scholar
  12. 12.
    Jain, M.D., Pradeep, S.N., Prakash, C., Raman, B.: Binary tree based linear time fingerprint matching. In: 2006 IEEE International Conference on Image Processing, pp. 309–312. IEEE, Oct 2006Google Scholar
  13. 13.
    Kamijo, M.: Classifying fingerprint images using neural network: Deriving the classification state. In: 1993 IEEE International Conference on Neural Networks, pp. 1932–937. IEEE (1993)Google Scholar
  14. 14.
    Kannavara, R., Bourbakis, N.G.: Fingerprint biometric authentication based on local global graphs. In: Proceedings of the IEEE 2009 National Aerospace & Electronics Conference (NAECON), pp. 200–204. IEEE, July 2009Google Scholar
  15. 15.
    Kaur, R., Sandhu, P.S., Kamra, A.: A novel method for fingerprint feature extraction. In: 2010 International Conference on Networking and Information Technology (ICNIT), pp. 1–5. IEEE, June 2010Google Scholar
  16. 16.
    Kawagoe, M., Tojo, A.: Fingerprint pattern classification. Pattern Recogn. 17(3), 295–303 (1984)CrossRefGoogle Scholar
  17. 17.
    Maio, D., Maltoni, D., Rizzi, S.: Dynamic clustering of maps in autonomous agents. IEEE Trans. Pattern Anal. Mach. Intell. 18(11), 1080–1091 (1996)CrossRefGoogle Scholar
  18. 18.
    Min, M.M. and Thein, Y.: Intelligent fingerprint recognition system by using geometry approach. In: 2009 International Conference on the Current Trends in Information Technology (CTIT), pp. 1–5. IEEE, Dec 2009Google Scholar
  19. 19.
    Moayer, B., Fu, K.S.: A tree system approach for fingerprint pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell. 3, 376–387 (1986)CrossRefGoogle Scholar
  20. 20.
    Vaikole, S., Sawarkar, S.D., Hivrale, S., Sharma, T.: Minutiae feature extraction from fingerprint images. In: 2009 IEEE International Advance Computing Conference, IACC 2009, pp. 691–696. IEEE, Mar 2009Google Scholar
  21. 21.
    Wahab, A., Chin, S.H., Tan, E.C.: Novel approach to automated fingerprint recognition. IEE Proc. Vis. Image Sign. Process. 145(3), 160–166 (1998)CrossRefGoogle Scholar
  22. 22.
    Zafar, W., Ahmad, T., Hassan, M.: Minutiae based fingerprint matching techniques. In: 2014 IEEE 17th International Multi-Topic Conference (INMIC), pp. 411–416. IEEE, Dec 2014Google Scholar
  23. 23.
    Zhang, P., Hu, J., Li, C., Bennamoun, M., Bhagavatula, V.: A pitfall in fingerprint bio-cryptographic key generation. Comput. Secur. 30(5), 311–319 (2011)CrossRefGoogle Scholar
  24. 24.
    Zhao, F., Tang, X.: Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction. Pattern Recogn. 40(4), 1270–1281 (2007)CrossRefGoogle Scholar
  25. 25.
    Zhong, W.B., Ning, X.B., Wei, C.J.: A fingerprint matching algorithm based on relative topological relationship among minutiae. In: 2008 International Conference on Neural Networks and Signal Processing, pp. 225–228. IEEE, June 2008Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • K. Anjana
    • 1
  • K. Praveen
    • 1
    Email author
  • P. P. Amritha
    • 1
  • M. Sethumadhavan
    • 1
  1. 1.TIFAC-CORE in Cyber Security, Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

Personalised recommendations