Texture Analysis for Skin Probability Maps Refinement

  • Michal Kawulok
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7329)


In this paper a new method for skin regions detection and segmentation is proposed. To improve the conventional color-based skin models, skin probability maps are subject to texture analysis using discriminative statistical features. Although the texture was utilized for skin detection in some of the existing methods, the main contribution of the work reported here is that the probability maps rather than the original color images are processed. The method has been validated in a series of experiments using two data sets. The obtained results are reported in the paper, and they confirm that the method is competitive.


Linear Discriminant Analysis Skin Region Reference Pixel Skin Detection Skin Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. Int. J. of Comp. Vision 46, 81–96 (2002)zbMATHCrossRefGoogle Scholar
  2. 2.
    Kakumanu, P., Makrogiannis, S., Bourbakis, N.G.: A survey of skin-color modeling and detection methods. Pattern Recognition 40(3), 1106–1122 (2007)zbMATHCrossRefGoogle Scholar
  3. 3.
    Kovac, J., Peer, P., Solina, F.: Human skin color clustering for face detection. In: EUROCON 2003. Computer as a Tool, vol. 2, pp. 144–148 (September 2003)Google Scholar
  4. 4.
    Tsekeridou, S., Pitas, I.: Facial feature extraction in frontal views using biometric analogies. In: Proc. of EUSIPCO 1998, pp. 315–318 (1998)Google Scholar
  5. 5.
    Hsu, R.-L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Anal. and Machine Intelligence 24(5), 696–706 (2002)CrossRefGoogle Scholar
  6. 6.
    Kukharev, G., Nowosielski, A.: Fast and efficient algorithm for face detection in colour images. Machine Graphics and Vision 13, 377–399 (2004)Google Scholar
  7. 7.
    Cheddad, A., Condell, J., Curran, K., Mc Kevitt, P.: A skin tone detection algorithm for an adaptive approach to steganography. Signal Processing 89(12), 2465–2478 (2009)zbMATHCrossRefGoogle Scholar
  8. 8.
    Greenspan, H., Goldberger, J., Eshet, I.: Mixture model for face-color modeling and segmentation. Pattern Recogn. Lett. 22, 1525–1536 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Khan, R., Hanbury, A., Stöttinger, J.: Skin detection: A random forest approach. In: Proc. 17th IEEE Int. Image Processing (ICIP) Conf., pp. 4613–4616 (2010)Google Scholar
  10. 10.
    Lee, J.S., Kuo, Y.M., Chung, P.C., Chen, E.L.: Naked image detection based on adaptive and extensible skin color model. Pattern Recognition 40, 2261–2270 (2007)zbMATHCrossRefGoogle Scholar
  11. 11.
    Phung, S.L., Chai, D., Bouzerdoum, A.: Adaptive skin segmentation in color images. In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 353–356 (2003)Google Scholar
  12. 12.
    Zhang, M.-J., Wang, W.-Q., Zheng, Q.-F., Gao, W.: Skin-color detection based on adaptive thresholds. In: Proceedings of the Third International Conference on Image and Graphics, ICIG 2004, pp. 250–253. IEEE Computer Society, Washington, DC (2004)CrossRefGoogle Scholar
  13. 13.
    Kawulok, M.: Dynamic Skin Detection in Color Images for Sign Language Recognition. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 112–119. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Kawulok, M.: Energy-based blob analysis for improving precision of skin segmentation. Multimedia Tools and Applications 49(3), 463–481 (2010)CrossRefGoogle Scholar
  15. 15.
    del Solar, J.-R., Verschae, R.: Skin detection using neighborhood information. In: Proc. IEEE International Conference on Automatic Face and Gesture Recognition, pp. 463–468 (2004)Google Scholar
  16. 16.
    Wang, X., Zhang, X., Yao, J.: Skin color detection under complex background. In: Proc. International Conference on Mechatronic Science, Electric Engineering and Computer (2011)Google Scholar
  17. 17.
    Abin, A.A., Fotouhi, M., Kasaei, S.: A new dynamic cellular learning automata-based skin detector. Multimedia Syst. 15(5), 309–323 (2009)CrossRefGoogle Scholar
  18. 18.
    Conci, A., Nunes, E., Pantrigo, J.J., Sánchez, Á.: Comparing color and texture-based algorithms for human skin detection. In: ICEIS (5), pp. 166–173 (2008)Google Scholar
  19. 19.
    Sigal, L., Sclaroff, S., Athitsos, V.: Skin color-based video segmentation under time-varying illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 862–877 (2003)CrossRefGoogle Scholar
  20. 20.
    Kawulok, M., Smolka, B.: Texture-adaptive image colorization framework. In: EURASIP Journal on Advances in Signal Processing 2011(99) (2011)Google Scholar
  21. 21.
    Seber, G.: Multivariate Observations. Wiley, New York (1984)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michal Kawulok
    • 1
  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

Personalised recommendations