access icon free Pre-registration of latent fingerprints based on orientation field

In this study, the authors present a hierarchical algorithm to register a partial fingerprint against a full fingerprint using only the orientation fields. In the first level, they shortlist possible locations for registering the partial fingerprint in the full fingerprint using a normalised correlation measure, taking various rotations into account. As a second level, on those candidate locations, they calculate three other similarity measures. They then perform score fusion for all the estimated similarity scores to locate the final registration. By registering a partial fingerprint against a full fingerprint, they can reduce the search space of the minutiae set in the full fingerprint, thereby improving the result of partial fingerprint identification, particularly for poor quality latent fingerprints. They report the rank identification improvements of two minutiae-based automated fingerprint identification systems on the National Institute of Standards and Technology (NIST)-Special Database 27 database when they use the authors hierarchical registration as a pre-alignment.

Inspec keywords: fingerprint identification; correlation methods; image registration; search problems

Other keywords: partial fingerprint registration; normalised correlation measure; partial fingerprint identification; full fingerprint; hierarchical algorithm; similarity measures; similarity scores; NIST-special database; score fusion; hierarchical registration; minutiae-based automated fingerprint identification systems; search space; latent fingerprints preregistration; rank identification improvements; orientation fields

Subjects: Combinatorial mathematics; Combinatorial mathematics; Image recognition; Computer vision and image processing techniques

References

    1. 1)
      • 21. Krish, R.P., Fierrez, J., Ramos, D., et al: ‘Evaluation of AFIS-ranked latent fingerprint matched template’. Sixth Pacific-Aim Symp. on Image and Video Technology, Mexico, Springer, (LNCS, 8333), November 2013, pp. 230241.
    2. 2)
      • 18. Yager, N., Amin, A.: ‘Evaluation of fingerprint orientation field registration algorithms’. Proc. Int. Conf. on Pattern Recognition (17th), 2004, vol. 4, pp. 641644.
    3. 3)
      • 30. Available at http://www.nist.gov/itl/iad/ig/nbis.cfm (NBIS Release 4.2.0).
    4. 4)
    5. 5)
      • 23. Bigun, J. (ed.): ‘Vision with direction: a systematic introduction to image processing and computer vision’ (Springer, 2005).
    6. 6)
      • 2. Maltoni, D., Maio, D., Jain, A., Prabhakar, S.: ‘Handbook of fingerprint recognition’ (Springer Publishing Company, New York, NY, 2009).
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 9. NIST: ‘Summary of the results of Phase I ELFT testing’. Available at http://biometrics.nist.gov/cslinks/latent/elft07/phase1.aggregate.pdf, September 2007.
    12. 12)
      • 11. Indovina, M., Dvornychenko, V., et al: ‘Evaluation of latent fingerprint technologies: extended feature sets (evaluation 2)’. Technical Report NISTIR 7859, NIST, 2012.
    13. 13)
      • 17. Nilsson, K., Bigun, J.: ‘Registration of fingerprints by complex filtering and by 1D projections of orientation images’, in Kanade, T., Jain, A., Ratha, N.K., (Eds.): ‘Audio-and video-based biometric person authentication’ (Springer Berlin Heidelberg, 2005), pp. 171183.
    14. 14)
    15. 15)
    16. 16)
      • 20. Garris, M., McCabe, R.: ‘NIST special database 27: fingerprint minutiae from latent and matching tenprint images’. Technical Report, NISTIR, 6534, 2000.
    17. 17)
      • 14. Krish, R.P., Fierrez, J., Ramos, D., et al: ‘Partial fingerprint registration for forensics using minutiae-generated orientation fields’. IEEE Second Int. Workshop on Biometrics and Forensics, Valletta, Malta, March 2014.
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • 12. Indovina, M., Dvornychenko, V., Hicklin, R.A., et al: ‘NIST evaluation of latent fingerprint technologies: extended feature sets [evaluation #1]’. Technical Report, NIST Interagency/Internal Report (NISTIR), 7775, April 2011.
    22. 22)
    23. 23)
      • 6. Fang, G., Srihari, S., Srinivasan, H., Phatak, P.: ‘Use of ridge points in partial fingerprint matching’. Proc. of SPIE: Biometric Technology for Human Identification IV, 2007.
    24. 24)
    25. 25)
    26. 26)
    27. 27)
      • 15. Krish, R.P., Fierrez, J., Ramos, D., et al: ‘Pre-registration for improved latent fingerprint identification’. Proc. IAPR/IEEE 22nd Int. Conf. on Pattern Recognition, ICPR, Stockholm, Sweden, August 2014, pp. 696701.
    28. 28)
      • 1. Holder, E., Robinson, L., Laub, J.: ‘The fingerprint sourcebook’ (US Department of Justice, Office of Justice Programs, National Institute of Justice, Washington, DC, 2011).
    29. 29)
    30. 30)
      • 8. NIST: ‘Evaluation of latent fingerprint technologies’. Available at http://www.nist.gov/itl/iad/ig/latent.cfm.
    31. 31)
      • 10. Indovina, M., Dvornychenko, V., Tabassi, E., et al: ‘An evaluation of automated latent finger-print identification technology (phase II)’. Technical Report, NIST Interagency/Internal Report (NISTIR), 75-77, April2009.
    32. 32)
      • 31. Available at http://www.biolab.csr.unibo.it (MCC SDK 1.4).
    33. 33)
    34. 34)
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