International Journal of Computer Vision

, Volume 46, Issue 1, pp 81–96 | Cite as

Statistical Color Models with Application to Skin Detection

  • Michael J. Jones
  • James M. Rehg


The existence of large image datasets such as the set of photos on the World Wide Web make it possible to build powerful generic models for low-level image attributes like color using simple histogram learning techniques. We describe the construction of color models for skin and non-skin classes from a dataset of nearly 1 billion labelled pixels. These classes exhibit a surprising degree of separability which we exploit by building a skin pixel detector achieving a detection rate of 80% with 8.5% false positives. We compare the performance of histogram and mixture models in skin detection and find histogram models to be superior in accuracy and computational cost. Using aggregate features computed from the skin pixel detector we build a surprisingly effective detector for naked people. Our results suggest that color can be a more powerful cue for detecting people in unconstrained imagery than was previously suspected. We believe this work is the most comprehensive and detailed exploration of skin color models to date.

skin detection color models histograms 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Athitsos, V., Swain, M.J., and Frankel, C. 1997. Distinguishing photographs and graphics on the world wide web. In Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries, San Juan, Puerto Rico, pp. 10–17.Google Scholar
  2. Chen, Q., Wu, H., and Yachida, M. 1995. Face detection by fuzzy pattern matching. In Proc. of Fifth Intl. Conf. on Computer Vision, Cambridge, MA, pp. 591–596.Google Scholar
  3. Cotton, S.D. and Claridge, E. 1996. Do all human skin colors lie on a defined surface within LMS space? Technical Report CSR-96-01, School of Computer Science, Univ. of Birmingham, UK.Google Scholar
  4. Forsyth, D.A. and Fleck, M.M. 1999. Automatic detection of human nudes. International Journal of Computer Vision, 32(1):63–77.Google Scholar
  5. Fukunaga, K. 1972. Introduction to Statistical Pattern Recognition. San Diago, CA: Academic Press.Google Scholar
  6. Gong, Y. and Sakauchi, M. 1995. Detection of regions matching specified chromatic features. Computer Vision and Image Understanding, 61(2):263–269.Google Scholar
  7. Jebara, T.S. and Pentland, A. 1997. Parameterized structure from motion for 3d adaptive feedback tracking of faces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp. 144–150.Google Scholar
  8. Kjeldsen, R. and Kender, J. 1996. Finding skin in color images. In Proceedings of the International Conference on Automatic Face and Gesture Recognition, Killington, VT, pp. 312–317.Google Scholar
  9. Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., and Poggio, T. 1997. Pedestrian detection using wavelet templates. In Proceedings of the Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp. 193–199.Google Scholar
  10. Quinlan, J.R. 1993. C4.5: Programs for Machine Learning, San Mateo, CA: Morgan Kauffman.Google Scholar
  11. Redner, R. and Walker, H. 1994. Mixture densities, maximum like-lihood, and the EM algorithm. SIAM Revew, 26:195–239.Google Scholar
  12. Rowley, H.A., Baluja, S., and Kanade, T. 1998. Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1):23–38.Google Scholar
  13. Schiele, B. and Waibel, A. 1995. Gaze tracking based on face-color. In Proceedings of the International Workshop on Automatic Face-and Gesture-Recognition, Zurich, Switzerland, pp. 344–349.Google Scholar
  14. Syeda-Mahmood, T. and Cheng, Y.Q. 1996. Indexing colored surfaces in images. In Proc. of Intl. Conf. on Pattern Recognition, Vienna, Austria.Google Scholar
  15. Van Gemert, M.J.C., Jacques, S.L., Sterenborg, H.J.C.M., and Star, W.M. 1989. Skin optics. IEEE Trans. on Biomedical Engineering, 36(12):1146–1154.Google Scholar
  16. Van Trees, H.L. 1968. Detection, Estimation, and Modulation Theory, vol. I. New York: Wiley.Google Scholar
  17. Wang, J.Z., Li, J., Wiederhold, G., and Firschein, O. 1997. System for screening objectionable images using daubechies wavelets and color histograms. In Proc. of the International Workshop on Interactive Distributed Multimedia Systems and Telecommunication Services, pp. 20–30.Google Scholar
  18. Yang, J., Lu, W., and Waibel, A. 1998. Skin-color modeling and adaptation. In Proceedings of the 3rd Asian Conference on Computer Vision, Hong Kong, China, pp. 687–694.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Michael J. Jones
    • 1
  • James M. Rehg
    • 2
  1. 1.Mitsubishi Electric Research LaboratoriesCambridge
  2. 2.College of ComputingGeorgia Institute of TechnologyAtlantaUSA

Personalised recommendations