A Region-Based Skin Color Detection Algorithm

  • Faliang Chang
  • Zhiqiang Ma
  • Wei Tian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4426)


In this paper, a new region-based algorithm for detecting skin color in static images is described. We choose the single Gaussian skin color model in the normalized r-g space after analyzing the distributions of skin color in six different 2-D chrominance spaces. Images are first segmented into patches using a improved fuzzy C-means algorithm, in which the local characteristic is adopted to constrain fuzzy functions, and a simple method for initializing clustering centriods is adopted. Then, the percentage of skin color pixels in each patch can be obtained. According to corresponding percentages, patches are classified as skin color regions or not.


skin color detection fuzzy C-means clustering color image segmentation 


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  1. 1.
    Kruppa, H., Bauer, M.A., Schiele, B.: Skin Patch Detection in Real-World Images. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 109–116. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Kovac, J., Peer, P., Solina, F.: Human Skin Colour Clustering for Face Detection. In: EUROCON 2003. Computer as a Tool. The IEEE Region 8, vol. 2, pp. 144–148 (2003)Google Scholar
  3. 3.
    Terrillon, J.-C., et al.: Comparative Performance of Different Skin Chrominance Models and Chrominance Spaces for the Automatic Detection of Human Faces in Color Images. In: Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 54–61. IEEE Computer Society Press, Los Alamitos (2000)CrossRefGoogle Scholar
  4. 4.
    Vezhnevets, V., Sazonov, V., Andreeva, A.: A Survey on Pixel-Based Skin Color Detection Techniques. In: Proceedings of 13th International Conference of Computer Graphics and Visualization Graphicon-2003, pp. 85–92 (2003)Google Scholar
  5. 5.
    Jones, M.J., Rehg, J.M.: Statistical Color Models with Application to Skin Detection. International Journal of Computer Vision 46(1), 81–96 (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Yang, M.-H., Ahuja, N.: Detecting Human Faces in Color Images. In: International Conference on Image Processing (ICIP), vol. 1, pp. 127–130 (1998)Google Scholar
  7. 7.
    Zhang, J., Modestino, J.W., Langan, D.A.: Maximum-Likelihood Parameter Estimation for Unsupervised Stochastic Model-Based Image Segmentation. IEEE Trans. Image Processing 3(4), 404–420 (1994)CrossRefGoogle Scholar
  8. 8.
    Xie, X.L., Beni, G.: A Validity Measure for Fuzzy Clustering. IEEE Trans. Pattern Anal. Machine Intell. 13(8), 841–847 (1991)CrossRefGoogle Scholar
  9. 9.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 2nd edn. Academic Press, London (2003)Google Scholar
  10. 10.
    Pal, N.R., Bezdek, J.C.: On Cluster Validity for Fuzzy c-Means Model. IEEE Trans. Fuzzy Systems 3(3), 370–379 (1995)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Faliang Chang
    • 1
  • Zhiqiang Ma
    • 1
  • Wei Tian
    • 1
  1. 1.School of Control Science and Engineering, Shandong UniversityP. R. China

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