Adapting Hausdorff Metrics to Face Detection Systems: A Scale-Normalized Hausdorff Distance Approach

  • Pablo Suau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3808)

Abstract

Template matching face detection systems are used very often as a previous step in several biometric applications. These biometric applications, like face recognition or video surveillance systems, need the face detection step to be efficient and robust enough to achieve better results. One of many template matching face detection methods uses Hausdorff distance in order to search the part of the image more similar to a face. Although Hausdorff distance involves very accurate results and low error rates, overall robustness can be increased if we adapt it to our concrete application. In this paper we show how to adjust Hausdorff metrics to face detection systems, presenting a scale-normalized Hausdorff distance based face detection system. Experiments show that our approach can perform an accurate face detection even with complex background or varying light conditions.

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References

  1. 1.
    Yang, M.H., Kriegman, D., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 24(1), 34–58 (2002)CrossRefGoogle Scholar
  2. 2.
    Lam, K., Yan, H.: Fast Algorithm for Locating Head Boundaries. J. Electronic Imaging 3(4), 351–359 (1994)CrossRefGoogle Scholar
  3. 3.
    Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust Face Detection Using the Hausdorff Distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing Images Using the Hausdorff Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(9), 850–853 (1993)CrossRefGoogle Scholar
  5. 5.
    Huttenlocher, D.P., Rucklidge, W.J.: A multi-resolution technique for comparing images using the Hausdorff distance, Technical Report 1321, Cornell University, Department of Computer Science (1992)Google Scholar
  6. 6.
    Kirchberg, K.J., Jesorsky, O., Frischholz, R.W.: Genetic Model Optimization for Hausdorff Distance-Based Face Localization. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol. 2359, pp. 103–111. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Shapiro, M.D., Blaschko, M.B.: On Hausdorff Distance Measures, Technical Report UM-CS-2004-071, Department of Computer Science, University of Massachusetts Amherst (2004)Google Scholar
  8. 8.
    Srisuk, S., Kurutach, W., Limpitikeat, K.: A Novel Approach for Robust, Fast and Accurate Face Detection. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS) 9(6), 769–779 (2001)MATHGoogle Scholar
  9. 9.
    Manian, V., Ross, A.: A Texture-based Approach to Face Detection. In: Biometric Consortium Conference (BCC), Crystal City, VA (September 2004)Google Scholar
  10. 10.
    Fröba, B., Küblbeck, C.: Robust Face Detection at Video Frame Rate Based on Edge Orientation Features. In: Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FGR 2002), Washington, USA, pp. 342–347 (2002)Google Scholar
  11. 11.
    Rosenblum, M., Yacoob, Y., Davis, L.: Human Expression Recognition from Motion using a Radial Basis Function Network Architecture. IEEE Transactions on Neural Networks 7(5), 1121–1138 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pablo Suau
    • 1
  1. 1.Departamento de Ciencia de la Computación e Inteligencia ArtificialUniversidad de AlicanteAlicanteSpain

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