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Skin Segmentation Using YUV and RGB Color Spaces

  • Received : 2013.04.17
  • Accepted : 2013.11.28
  • Published : 2014.06.30

Abstract

Skin detection is used in many applications, such as face recognition, hand tracking, and human-computer interaction. There are many skin color detection algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other's thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others.

Keywords

References

  1. A. Cheddad, J. Condell, K. Curran, and P. McKevitt, "A new colour space for skin tone detection," in Proceedings of the 16th IEEE International Conference on Image Processing, Cairo, Egypt, 2009, pp. 497-500.
  2. G. Kukharev and A. Nowosielski, "Visitor identification: elaborating real time face recognition system," in Proceedings of the 12th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, Plzen-Bory, Czech Republic, 2004, pp. 157-164.
  3. K. Sobottka and I. Pitas, "A novel method for automatic face segmentation, facial feature extraction and tracking," Signal Processing: Image Communication, vol. 12, no. 3, pp. 263-281, 1998. https://doi.org/10.1016/S0923-5965(97)00042-8
  4. R. M. Jusoh, N. Hamzah, M. H. Marhaban, and N. M. A. Alias, "Skin detection based on thresholding in RGB and hue component," in IEEE Symposium on Industrial Electronics & Applications, Penang, Malaysia, 2010, pp. 515-517.
  5. K. H. B. Ghazali, J. Ma, and R. Xiao, "An innovative face detection based on skin color segmentation," International Journal of Computer Applications, vol. 34, no. 2, pp. 6-10, 2011.
  6. A. S. Ghotkar and G. K. Kharate, "Hand segmentation techniques to hand gesture recognition for natural human computer interaction," International Journal of Human Computer Interaction, vol. 3, no. 1, pp. 15-25, 2012.
  7. B. N. Jagadesh, K. S. Rao, Ch. Satyanarayana, and G. V. S. RajKumar, "Skin colour segmentation using finite bivariate Pearsonian type-Iib mixture model and K-means," Signal & Image Processing, vol. 3, no. 4, pp. 37-49, 2012.
  8. D. Chai and K. N. Ngan, "Face segmentation using skin-color map in videophone applications," IEEE Transactions on Circuits and Systems for Video Technology, vol. 9, no. 4, pp. 551-564, 1999. https://doi.org/10.1109/76.767122
  9. D. Marius, S. Pennathur, and K. Rose (2003). Face Detection Using Color Thresholding, and Eigenimage Template Matching [Online]. Available: http://scien.stanford.edu/pages/labsite/2003/ee368/Project/slides/ee368group15.ppt
  10. J. J. de Dios and N. Garcia, "Fast face segmentation in component color space," in Proceedings of the International Conference on Image Processing, Singapore, 2004, pp. 191-194.
  11. A. H. K. Almohair, A. R. Ramli, A. M. Elsadig, and B. S. J. Hashim, "Skin detection in luminance images using threshold technique," International Journal of Imaging Science and Engineering, vol. 1, no. 1, pp. 32-35, 2007.
  12. M. S. Iraji and A. Yavari, "Skin color segmentation in fuzzy YCBCR color space with the Mamdani inference," American Journal of Scientific Research, vol. 2011, no. 7, pp. 131-137, 2011.
  13. C. Prema and D. Manimegalai, "A novel skin tone detection using hybrid approach by new color space," International Journal of Computer Applications, vol. 46, no. 7, pp. 15-19, 2012.
  14. J. Kovac, P. Peer, and F. Solina, "Human skin color clustering for face detection," in The IEEE Region 8 EUROCON 2003: Computer as a Tool, Ljubljana, Slovenia, 2003, pp. 144-148 vol.2.
  15. C. Prema and D. Manimegalai, "Survey on skin tone detection using color spaces," International Journal of Applied Information Systems, vol. 2, no. 2, pp. 18-26, 2012.
  16. A. Hanbury, "A 3D-polar coordinate colour representation well adapted to image analysis," in Image Analysis: 13th Scandinavian Conference, SCIA 2003 Halmstad, Sweden, June 29-July 2, 2003 Proceedings (Lecture Notes in Computer Science Vol. 2749), J. Bigun and T. Gustavsson, Eds., Heidelberg: Springer Berlin, 2003, pp. 804-811.
  17. R. Khan, Z. Khan, M. Aamir, and S. Q. Sattar, "Static filtered skin detection," International Journal of Computer Science Issues, vol. 9, no. 2, pp. 257-261, 2012.
  18. B. C. Ennehar, O. Brahim, and T. Hicham, "An appropriate color space to improve human skin detection," INFOCOMP Journal of Computer Science, vol. 9, no. 4, pp. 1-10, 2010.
  19. D. A. Lyon and N. Vincent, "Interactive embedded face recognition," Journal of Object Technology, vol. 8, no. 1, pp. 23-53, 2009.
  20. T. Abd El-Hafeez, "A new system for extracting and detecting skin color regions from PDF documents," International Journal on Computer Science and Engineering, vol. 2, no. 9, pp. 2838-2846, 2010.
  21. M. R. Tabassum, A. U. Gias, M. M. Kamal, S. Islam, H. M. Muctadir, M. Ibrahim, A. K. Shakir, A. Imran, S. Islam, M. G. Rabbani, S. M. Khaled, M. S. Islam, and Z. Begum, "Comparative study of statistical skin detection algorithms for sub-continental human images," Information Technology Journal, vol. 9, no. 4, pp. 811-817, 2010. https://doi.org/10.3923/itj.2010.811.817
  22. F. H. Xiang and S. A. Suandi, "Fusion of multi color space for human skin region segmentation," International Journal of Information and Electronics Engineering, vol. 3, no. 2, pp. 172-174, 2013.
  23. J. J. de Dios and N. Garcia, "Face detection based on a new color space YCgCr," in Proceedings of the International Conference on Image Processing, Barcelona, Spain, 2003, pp. III-909-III-912.
  24. Labeled Faces in the Wild (Accessed 2012, August 25) [Online]. Available: http://vis-www.cs.umass.edu/lfw/
  25. Y. Guoliang, L. Huan, Z. Li, and C. Yue, "Research on a skin color detection algorithm based on self-adaptive skin color model," in Proceedings of the International Conference on Communications and Intelligence Information Security, Nanning, China, 2010, pp. 266-270.

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