The VIRSBS project: Visual intelligent recognition for secure banking services

  • M. Tistarelli
  • E. Grossog
  • I. Bigun
  • C. Sacerdoti
  • J. Santos-Victor
  • D. Vernon
Special Session on European Projects
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

Secure access control is a key issue in banking services. Magnetic cards and personal identification numbers, currently adopted for accessing automatic tellers, do not provide a sufficient degree of security and are likely a source for unauthorized operations. As far as the access to restricted areas is concerned, it usually requires direct surveillance by guards or indirect surveillance by a human operator through a monitoring system. It is often difficult, due to fatigue or other distracting factors, to guarantee continuous and high performance in this task. The VIRSBS Reactive LTR project faces the problem of the automatic detection of person's identity by using advanced computer vision techniques. The goal of the VIRSBS project is to realize a prototype autonomous station for personal identification. This station will include all the features required to be integrated into a new generation of automated security check-point along corridors, passageways or access doors, and in the next-generation of automatic teller machines. This prototype will be used to perform a significant set of statistical tests on personal identification.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    I. Craw, D. Tock, and A. Bennett. Finding face features. In Proc. of second European Conference on Computer Vision pages 92–96, S. Margherita Ligure (Italy), 1992. Springer Verlag.Google Scholar
  2. 2.
    R. Brunelli and T. Poggio. Face recognition: Features versus templates. IEEE Trans. on PAMI, PAMI-15(10):1042–1052, 1993.Google Scholar
  3. 3.
    M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71–79, March 1991.Google Scholar
  4. 4.
    G. Sandini and M. Tistarelli. Vision and space-variant sensing. In H. Wechsler, editor, Neural Netivorks for Perception: Human and Machine Perception. Academic Press, 1991.Google Scholar
  5. 5.
    F. Tong and Z.N. Li. The reciprocal-wedge transform for space-variant sensing. In 4th IEEE Intl. Conference on Computer Vision, pages 330—334, Berlin, 1993.Google Scholar
  6. 6.
    E. L. Schwartz. Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception. Biological Cybernetics, 25:181–194, 1977.CrossRefPubMedGoogle Scholar
  7. 7.
    C. F. R. Weiman and G. Chaikin. Logarithmic spiral grids for image processing and display. Comp. Graphic and Image Process., 11:197–226, 1979.CrossRefGoogle Scholar
  8. 8.
    M. Turk and A. Pentland. Face recognition using eigenfaces. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, pages 586–591, Lahaina, Maui, Hawaii, 1991. IEEE Computer Society Press.Google Scholar
  9. 9.
    T. Nguyen and T.S. Huang. Segmentation, grouping and feature detection for face image analysis. In Proc. of the First IEEE International Symposium on Computer Vision, pages 593–598, Miami (FL), 1995.Google Scholar

Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • M. Tistarelli
    • 1
  • E. Grossog
    • 1
  • I. Bigun
    • 2
  • C. Sacerdoti
    • 3
  • J. Santos-Victor
    • 4
  • D. Vernon
    • 5
  1. 1.DIST, University of GenoaGenoaItaly
  2. 2.École Polytechnique Fédérale de LausanneSwitzerland
  3. 3.Logitron S.r.I.Italy
  4. 4.Instituto Superior Técnico de LisboaPortugal
  5. 5.Maynooth CollegeIreland

Personalised recommendations