A Novel Region Based Liveness Detection Approach for Fingerprint Scanners

  • Brian DeCann
  • Bozhao Tan
  • Stephanie Schuckers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

Biometric scanners have become widely popular in providing security to information technology and entry to otherwise sensitive locations. However, these systems have been proven to be vulnerable to spoofing, or granting entry to an imposter using fake fingers. While matching algorithms are highly successful in identifying the unique fingerprint biometric of an individual, they lack the ability to determine if the source of the image is coming from a living individual, or a fake finger, comprised of PlayDoh, silicon, gelatin or other material. Detection of liveness patterns is one method in which physiological traits are identified in order to ensure that the image received by the scanner is coming from a living source. In this paper, a new algorithm for detection of perspiration is proposed. The method quantifies perspiration via region labeling methods, a simple computer vision technique. This method is capable of extracting observable trends in live and spoof images, generally relating to the differences found in the number and size of identifiable regions per contour along a ridge or valley segment. This approach was tested on a optical fingerprint scanner, Identix DFR2100. The dataset includes a total of 1526 live and 1588 spoof fingerprints, arising from over 150 unique individuals with multiple visits. Performance was evaluated through a neural network classifier, and the results are compared to previous studies using intensity based ridge and valley liveness detection. The results yield excellent classification, achieving overall classification rates greater than 95.5%. Implementation of this liveness detection method can greatly improve the security of fingerprint scanners.

Keywords

Biometrics fingerprint liveness detection neural network 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jain, A., Bolle, R., Pankanti, S.: Biometrics: Personal Identification in Networked Society. Springer, Heidelberg (1999)Google Scholar
  2. 2.
    Schuckers, S.A.C.: Spoofing and anti-spoofing measures. Information Security Technical Report 7(4), 56–62 (2002)Google Scholar
  3. 3.
    Maltoni, D., Maio, D., Jain, A., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, Nu (2003)Google Scholar
  4. 4.
    Antonelli, A., Cappelli, R., Maio, D., Maltoni, D.: Fake Finger Detection by Skin Distortion Analysis. IEEE Transactions on Information Forensics and Security 1(3), 360–373 (2006)Google Scholar
  5. 5.
    Chen, Y., Jain, A., Dass, S.: Fingerprint Deformation for Spoof Detection. In: Proc. of Biometrics Symposium (BSYM 2005), Arlington, VA, September 19-21 (2005)Google Scholar
  6. 6.
    Zhang, Y., Tian, J., Chen, X., Yang, X., Shi, P.: Fake Finger Detection Baed on Thin-late Spline Distortion Model. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 742–749. Springer, Heidelberg (2007)Google Scholar
  7. 7.
    Jia, J., Cai, L.: Fake Finger Detection Based on Time-series Fingerprint Image Analysis. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. LNCS, vol. 4681, pp. 1140–1150. Springer, Heidelberg (2007)Google Scholar
  8. 8.
    Uchida, K.: Image-Based Appraoch to Fingerprint Acceptability Assessment. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 294–300. Springer, Heidelberg (2004)Google Scholar
  9. 9.
    Jin, C., Kim, H., Elliott, S.: Liveness Detection of Fingerprint Based on Band-Selective Fourier Spectrum. In: Nam, K.-H., Rhee, G. (eds.) ICISC 2007. LNCS, vol. 4817, pp. 168–179. Springer, Heidelberg (2007)Google Scholar
  10. 10.
    Choi, H., Kang, R., CHoi, K., Kim, J.: Aliveness Detection of Fingerprints using Multiple Static Features. In: Proc. of World Academy of Science, Engineering and Technology, July 2007, vol. 22 (2007)Google Scholar
  11. 11.
    Moon, Y.S., Chen, J.S., Chan, K.C., So, K., Woo, K.C.: Wavelet based fingerprint liveness detection. Electronic Letters 41(20), 1112–1113 (2005)Google Scholar
  12. 12.
    Coli, P., Marcialis, G.L., Roli, F.: Power spectrum-based fingerprint vitality detection. In: IEEE Workshop on AutoID (June 2007)Google Scholar
  13. 13.
    Derakhshani, R., Schuckers, S.A.C., Hornak, L., O’Gorman, L.: Determination of vitality from a noninvasive biomedical measurement for use in fingerprint scanners. Pattern Recognition 36(2) (2003)Google Scholar
  14. 14.
    Tan, B., Schuckers, S.: Liveness Detection for Fingerprint Scanners Based on the Statistics of Wavelet Signal Processing. In: IEEE CVPRW (June 2006)Google Scholar
  15. 15.
    Tan, B., Schuckers, S.: A New Approach for Liveness Detection in Fingerprint Scanners Based on Valley Noise Analysis. SPIE Journal of Electronic Imaging 17(1) (2008)Google Scholar
  16. 16.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Brian DeCann
    • 1
  • Bozhao Tan
    • 1
  • Stephanie Schuckers
    • 1
  1. 1.Clarkson UniversityPotsdamUSA

Personalised recommendations