Fingerprint Indexing Based on Combination of Novel Minutiae Triplet Features

  • Wei Zhou
  • Jiankun Hu
  • Song Wang
  • Ian Petersen
  • Mohammed Bennamoun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8792)


Fingerprint indexing is a process of pre-filtering the template database before matching. The most common features used for fingerprint indexing are based on minutiae triplets. In this paper, we investigated the indexing performance based on some commonly used features of minutiae triplets and proposed to combine these features with some novel features of minutiae triplets for fingerprint indexing. Experiments on FVC 2000 DB2a and 2002 DB1a show that the proposed indexing method can perform better than state-of-the-art schemes for full fingerprint indexing, meanwhile, experimental results on NIST SD 14 show that the performance is improved significantly after the new features are added to the feature space, and is fairly good even for partial fingerprint indexing.


Query Image Penetration Rate Indexing Scheme Query Sample Keystroke Dynamic 
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  1. 1.
    Xi, K., Hu, J., Han, F.: Mobile device access control: an improved correlation based face authentication scheme and its java me application. Concurrency and Computation: Practice and Experience 24(10), 1066–1085 (2012)CrossRefGoogle Scholar
  2. 2.
    Xi, K., Tang, Y., Hu, J.: Correlation keystroke verification scheme for user access control in cloud computing environment. Comput. J. 54(10), 1632–1644 (2011)CrossRefGoogle Scholar
  3. 3.
    Sufi, F., Khalil, I.: Faster person identification using compressed ecg in time critical wireless telecardiology applications. J. Network and Computer Applications 34(1), 282–293 (2011)CrossRefGoogle Scholar
  4. 4.
    Sufi, F., Khalil, I.: An automated patient authentication system for remote telecardiology. In: International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2008, pp. 279–284 (December 2008)Google Scholar
  5. 5.
    Sufi, F., Khalil, I., Hu, J.: Ecg-based authentication. In: Stavroulakis, P., Stamp, M. (eds.) Handbook of Information and Communication Security, pp. 309–331. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Ahmad, T., Hu, J., Wang, S.: Pair-polar coordinate-based cancelable fingerprint templates. Pattern Recogn. 44(10-11), 2555–2564 (2011)CrossRefGoogle Scholar
  7. 7.
    Wang, S., Hu, J.: Alignment-free cancelable fingerprint template design: A densely infinite-to-one mapping (ditom) approach. Pattern Recogn. 45(12), 4129–4137 (2012)CrossRefGoogle Scholar
  8. 8.
    Xi, K., Ahmad, T., Han, F., Hu, J.: A fingerprint based bio-cryptographic security protocol designed for client/server authentication in mobile computing environment. Journal of Security and Communication Networks 4(5), 487–499 (2011)CrossRefGoogle Scholar
  9. 9.
    Xi, K., Hu, J.: Introduction to Bio-cryptography. In: Handbook of Information and Communication Security. Springer (2010)Google Scholar
  10. 10.
    Yang, W., Hu, J., Wang, S., Stojmenovic, M.: An alignment-free fingerprint bio-cryptosystem based on modified voronoi neighbor structures. Pattern Recognition 47(3), 1309–1320 (2014)CrossRefGoogle Scholar
  11. 11.
    Wang, S., Hu, J.: Design of alignment-free cancelable fingerprint templates via curtailed circular convolution. Pattern Recognition 47(3), 1321–1329 (2014)CrossRefGoogle Scholar
  12. 12.
    Yang, W., Hu, J., Wang, S., Yang, J.: Cancelable fingerprint templates with delaunay triangle-based local structures. In: Wang, G., Ray, I., Feng, D., Rajarajan, M. (eds.) CSS 2013. LNCS, vol. 8300, pp. 81–91. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Yang, W., Hu, J., Wang, S.: A finger-vein based cancellable bio-cryptosystem. In: Lopez, J., Huang, X., Sandhu, R. (eds.) NSS 2013. LNCS, vol. 7873, pp. 784–790. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Lumini, A., Maio, D., Maltoni, D.: Continuous versus exclusive classification for fingerprint retrieval. Pattern Recogn. Lett. 18(10), 1027–1034 (1997)CrossRefGoogle Scholar
  15. 15.
    Cappelli, R., Lumini, A., Maio, D., Maltoni, D.: Fingerprint classification by directional image partitioning. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 402–421 (1999)CrossRefGoogle Scholar
  16. 16.
    Bhanu, B., Tan, X.: Fingerprint indexing based on novel features of minutiae triplets. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 616–622 (2003)CrossRefGoogle Scholar
  17. 17.
    de, J.B., Bazen, A.M., Gerez, S.H.: Indexing fingerprint databases based on multiple features. In: Proceedings SAFE, ProRISC, SeSens 2001, Utrecht, The Netherlands, STW, pp. 300–306 (November 2001)Google Scholar
  18. 18.
    Wang, Y., Hu, J., Phillips, D.: A fingerprint orientation model based on 2D fourier expansion (fomfe) and its application to singular-point detection and fingerprint indexing. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 573–585 (2007)CrossRefGoogle Scholar
  19. 19.
    Feng, J., Jain, A.K.: Filtering large fingerprint database for latent matching. In: Proc. Int. Conf. on Pattern Recognition (ICPR 2008), pp. 1–4 (2008)Google Scholar
  20. 20.
    Jain, A.K., Feng, J.: Latent fingerprint matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(1), 88–100 (2011)CrossRefGoogle Scholar
  21. 21.
    Yuan, B., Su, F., Cai, A.: Fingerprint retrieval approach based on novel minutiae triplet features. In: 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 170–175 (2012)Google Scholar
  22. 22.
    Paulino, A.A., Liu, E., Cao, K., Jain, A.K.: Latent fingerprint indexing: Fusion of level 1 and level 2 features. In: Biometrics: Theory, Applications and Systems, Washington, D.C. (2013)Google Scholar
  23. 23.
    Wang, Y., Hu, J.: Global ridge orientation modeling for partial fingerprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 72–87 (2011)CrossRefGoogle Scholar
  24. 24.
    VeriFinger: Verifinger sdk (2013),
  25. 25.
    Germain, R., Califano, A., Colville, S.: Fingerprint matching using transformation parameter clustering. IEEE Computational Science Engineering 4(4), 42–49 (1997)CrossRefGoogle Scholar
  26. 26.
    Liang, X., Bishnu, A., Asano, T.: A robust fingerprint indexing scheme using minutia neighborhood structure and low-order delaunay triangles. IEEE Transactions on Information Forensics and Security 2(4), 721–733 (2007)CrossRefGoogle Scholar
  27. 27.
    Iloanusi, O., Gyaourova, A., Ross, A.: Indexing fingerprints using minutiae quadruplets. In: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 127–133 (2011)Google Scholar
  28. 28.
    Shuai, X., Zhang, C., Hao, P.: Fingerprint indexing based on composite set of reduced sift features. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4 (2008)Google Scholar
  29. 29.
    Jiang, X., Liu, M., Kot, A.: Fingerprint retrieval for identification. IEEE Transactions on Information Forensics and Security 1(4), 532–542 (2006)CrossRefGoogle Scholar
  30. 30.
    Liu, M., Yap, P.T.: Invariant representation of orientation fields for fingerprint indexing. Pattern Recogn. 45(7), 2532–2542 (2012)CrossRefGoogle Scholar
  31. 31.
    SD14: Nist special database 14 (2013),
  32. 32.
    NBIS: Nist biometric image software (2013),

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Wei Zhou
    • 1
  • Jiankun Hu
    • 1
  • Song Wang
    • 2
  • Ian Petersen
    • 1
  • Mohammed Bennamoun
    • 3
  1. 1.School of Engineering and Information TechnologyThe University of New South WalesCanberraAustralia
  2. 2.School of Engineering and Mathematical SciencesLaTrobe UniversityMelbourneAustralia
  3. 3.School of Computer Science and Software EngineeringThe University of Western AustraliaPerthAustralia

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