Latent Fingerprint Matching: Fusion of Rolled and Plain Fingerprints

  • Jianjiang Feng
  • Soweon Yoon
  • Anil K. Jain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

Law enforcement agencies routinely collect both rolled and plain fingerprints of all the ten fingers of suspects. These two types of fingerprints complement each other, since rolled fingerprints are of larger size and contain more minutiae, and plain fingerprints are less affected by distortion and have clearer ridge structure. It is widely known in the law enforcement community that searching both rolled and plain fingerprints can improve the accuracy of latent matching, but, this does not appear to be a common practice in law enforcement. To our knowledge, only rank level fusion option is provided by the vendors. There has been no systematic study and comparison of different fusion techniques. In this paper, multiple fusion approaches at three different levels (rank, score and feature) are proposed to fuse rolled and plain fingerprints. Experimental results in searching 230 latents in the NIST SD27 against a database of 4,180 pairs of rolled and plain fingerprints show that most of the fusion approaches can improve the identification performance. The greatest improvement was obtained by boosted max fusion at the score level, which reaches a rank-1 identification rate of 83.0%, compared to the rank-1 rate of 57.8% for plain and 70.4% for rolled prints.

Keywords

Latent fingerprint rolled fingerprint plain fingerprint fusion minutiae matching 

References

  1. 1.
    Lee, H.C., Gaensslen, R.E. (eds.): Advances in Fingerprint Technology. CRC Press, New York (2001)Google Scholar
  2. 2.
    Komarinski, P.: Automated Fingerprint Identification Systems (AFIS). Academic Press, London (2004)Google Scholar
  3. 3.
    Parziale, G., Diaz-Santana, E.: The Surround Imager: a multi-camera touchless device to acquire 3D rolled-equivalent fingerprints. In: Zhang, D., Jain, A.K. (eds.) ICB 2006. LNCS, vol. 3832, pp. 244–250. Springer, Heidelberg (2006)Google Scholar
  4. 4.
    NIST: Evaluation of Fast Tenprint Capture Devices, http://fingerprint.nist.gov/FTcapture/index.html
  5. 5.
    Wood, S.S., Wilson, C.L.: Studies of plain-to-rolled fingerprint matching using the NIST algorithmic test bed (ATB). NISTIR 7112 (2004)Google Scholar
  6. 6.
    Swann, S.: Needs and applications of latents at FBI/CJIS, http://www.itl.nist.gov/iad/894.03/latent/workshop/proc/P6_Swann_LatentOverview_2.pdf
  7. 7.
    NIST: Summary of results from ELFT07 phase I testing (2007), http://fingerprint.nist.gov/latent/elft07/phase1_aggregate.pdf
  8. 8.
    Jain, A.K., Feng, J., Nagar, A., Nandakumar, K.: On matching latent fingerprints. In: Proc. CVPR Workshop on Biometrics, pp. 1–8 (2008)Google Scholar
  9. 9.
    Ross, A., Nandakumar, K., Jain, A.K.: Handbook of Multibiometrics. Springer, Heidelberg (2006)Google Scholar
  10. 10.
    Wilson, C., et al.: Fingerprint vendor technology evaluation 2003: Summary of results and analysis report. NISTIR 7123 (2004)Google Scholar
  11. 11.
    Marcialis, G.L., Roli, F.: Fingerprint verification by decision-level fusion of optical and capacitive sensors. In: Proc. ECCV Workshop on Biometric Authentication, pp. 307–317 (2004)Google Scholar
  12. 12.
    Jain, A.K., Ross, A.: Fingerprint mosaicking. In: Proc. International Conference on Acoustic Speech and Signal Processing (ICASSP), pp. 4064–4067 (2002)Google Scholar
  13. 13.
    Ross, A., Jain, A.K., Reisman, J.: A hybrid fingerprint matcher. Pattern Recognition 36(7), 1661–1673 (2003)Google Scholar
  14. 14.
    Hara, M., Toyama, H.: Print image synthesizing device and method, and print image synthesizing program. US Patent Application Publication No. 2006/002595A1 (2006)Google Scholar
  15. 15.
    Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Trans. Pattern Analysis and Machine Intelligence 16(1), 66–75 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jianjiang Feng
    • 1
  • Soweon Yoon
    • 1
  • Anil K. Jain
    • 1
  1. 1.Department of Computer Science and EngineeringMichigan State UniversityUSA

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