Skin Detection in Videos in the Spatial-Range Domain

  • Javier Ruiz-del-Solar
  • Rodrigo Verschae
  • Daniel Kottow
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

Most of the already proposed skin detection approaches are based on the same pixel-wise paradigm, in which each image pixel is individually analyzed. We think that this paradigm should be extended; context information should be incorporated in the skin detection process. Following this idea, in this article is proposed a robust and fast skin detection approach that uses spatial and temporal context. Spatial context implies that the decision about the class (skin or non-skin) of a given pixel considers information about the pixel’s neighbors. Temporal context implies that skin detection is carried out considering not only pixel values from the current frame, but also taking into account past frames and general background reference information.

References

  1. 1.
    Albiol, A., Torres, L., Delp, E.: Optimum Color Spaces for Skin Detection. In: IEEE Int. Conf. on Image Proc. – ICIP 2001, Greece (2001)Google Scholar
  2. 2.
    Jedynak, B., Zheng, H., Daoudi, M.: Statistical Models for Skin Detection. In: IEEE Workshop Statistical Analysis in Computer Vision, together with CVPR 2003 (2003)Google Scholar
  3. 3.
    Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. Int. Journal of Computer Vision 46(1), 81–96 (2002)MATHCrossRefGoogle Scholar
  4. 4.
    Kottow, D., Köppen, M., Ruiz-del-Solar, J.: A Background Maintenance Model in the Spatial-Range Domain. In: 2nd Workshop on Statistical Methods in Video Processing (ECCV 2004 associated workshop), Prague, Czech Republic, May 16 (2004)Google Scholar
  5. 5.
    Shin, M., Chang, K., Tsap, L.: Does colorspace transformation make any difference on skin detection? In: Proc. IEEE Workshop on Appl. of Computer Vision, Florida, USA (2002)Google Scholar
  6. 6.
    Sigal, L., Sclaroff, S., Athisos, V.: Skin color-based video segmentation under time-varying illumination. IEEE Trans. on Pattern Anal. and Machine Int. 26(7), 862–877 (2004)CrossRefGoogle Scholar
  7. 7.
    Spillman, L., Werner, J. (eds.): Visual Perception: The Neurophysiological Foundations. Academic Press, London (1990)Google Scholar
  8. 8.
    Yang, M.H., Ahuja, N.: Detecting human faces in color images. In: Proc. IEEE Int. Conf. on Image Processing, Chicago, Illinois, USA, vol. 1, pp. 127–130 (1998)Google Scholar
  9. 9.
    Zhu, Q., Cheng, K.-T., Wu, C.-T., Wu, Y.-L.: Adaptive Learning of an Accurate Skin-Color Model. In: Proc. 6th IEEE Int. Conf. on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 37–42 (2004)Google Scholar
  10. 10.
    Ruiz-del-Solar, J., Verschae, J.R.: Robust Skin Detection using Neighborhood Information. In: Int. Conf. on Image Processing, Singapore, October 24-27 (2004)Google Scholar
  11. 11.
    Martinkauppi, B.: Face Color under Varying Illumination – Analysis and Applications. Doctoral Thesis, University of Oulu, Finland (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Javier Ruiz-del-Solar
    • 1
    • 2
  • Rodrigo Verschae
    • 1
    • 2
  • Daniel Kottow
    • 1
  1. 1.Department of Electrical EngineeringUniversidad de Chile 
  2. 2.Center for Web Research, Department of Computer ScienceUniversidad de Chile 

Personalised recommendations