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Attentive Face Detection and Recognition

  • Volker Krüger
  • Udo Mahlmeister
  • Gerald Sommer
Part of the Informatik aktuell book series (INFORMAT)

Abstract

In this paper we will present an approach for the attentive detection and recognition of faces in gray-value images. The approach is biologically motivated. The attentive face system, as we call it, shows great robustness with respect to scale, rotation, viewing orientation, changes in illumination, facial expressions, partial occlusions and other distortions caused, e.g., by glasses or a beard. The system has knowledge of several templates of different persons as well as of their exact relative positions. In a first low-level step the system detects relevant image features by evaluating a similarity measurement between local image features and known facial templates. In a second high-level step the system verified the consistency of these features by using the knowledge of the exact relative positions of the templates and reports whether a face was recognized, detected or whether no face was present.

Keywords

face detection face recognition 

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References

  1. 1.
    Roberto Brunelli, Tomaso Poggio. Face recognition: Features versus templates. IEEE Trans. Pattern Analysis and Machine Intelligence, 15 (10): 1042–1052, Oct. 1993.CrossRefGoogle Scholar
  2. 2.
    G. H. Granlund and G. D. Knutsson. Signal Processing For Computer Vision. Kluwer Academic Publisher, 1995.Google Scholar
  3. 3.
    U. Mahlmeister, M. Sandgaard, and G. Sommer. Sample-guided progressive image coding. http://www.informatik.uni-kiel.de/˜uhm/research/icprl.ps.gz, accepted in ICPR’98, Brisbane Australia, 1997.Google Scholar
  4. 4.
    M. Sandgaard. Attentionsgesteuerte progressive Übertragung von Bildern. Master’s thesis, Institute of Computer Science and Applied Mathematics, University of Kiel, Germany, 1997. in German.Google Scholar
  5. 5.
    B. Schiele and J. L. Crowley. Object recognition using multidimensional receptive field histograms. In Proc. Fourth European Conference on Computer Vision, Cambridge, UK, April 15–18, 1996.Google Scholar
  6. 6.
    E. P. Simoncelli and W. T. Freeman. The steerable pyramid: a flexible architecture for multi-scale derivative computation. Technical report, GRAPS Laboritory, Philadelphia, 1995.Google Scholar
  7. 7.
    E. P. Simoncelli, W. T. Freeman, E. A. Adelson, and D. J. Heger. Shiftable multiscale transforms. IEEE Trans. Information Theory, 38 (2): 587–607, 1992.CrossRefGoogle Scholar
  8. 8.
    M. J. Swain and D. H. Ballard. Color indexing. International Journal of Computer Vision, 7(1):ll–32, 1991.CrossRefGoogle Scholar
  9. 9.
    L. Wiskott, J. M. Fellous, N. Krüger, and C. v. d. Malsburg. Face recognition and gender determination. In International Workshop on Automatic Face- and Gesture-Recognition, Zurich, Switzerland, June 26–28, 1995.Google Scholar
  10. 10.
    L. Wiskott, J. M. Fellous, N. Krüger, and C. v. d. Malsburg. Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Analysis and Machine Intelligence, 19 (7): 775–779, July 1997.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Volker Krüger
    • 1
  • Udo Mahlmeister
    • 1
  • Gerald Sommer
    • 1
  1. 1.University of Kiel, GermanyKielGermany

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