Skin Detection Using Hybrid Colour Space of RGB-H-CMYK

  • Ashish KumarEmail author
  • P. Shanmugavadivu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 713)


Skin detection is an essential step in human face detection and/or recognition, using digital image processing techniques. This paper presents a new human skin detection technique, termed as RGB-H-CMYK that uses triple colour spaces of an image, namely RGB (Red, Blue and Green), H (Hue of HSV) and CMYK (Cyan, Magenta, Yellow and Black). In this proposed method, threshold-based rules are applied on RGB, H and CMYK for skin classification. The input image in these three hybrid colour schemes is explored in different combination such as RC (RGB and CMYK), RH (RGB and H) and RHC (RGB and H and CMYK). The RHC_Vote qualifies the current pixel as skin pixel when at least two rules vote for it. The computational merit of this hybrid colour scheme-based skin detection is validated on the real-time dataset and ECU skin database. The average Recall and Accuracy of this method is recorded as 85% and 89%, respectively. This approach is confirmed to have an edge over its competitive methods, as it promises object localization, based on neighbourhood intensities without using the computationally complex approaches such as facial texture and geometric properties.


RGB HSV CMYK RGB-H-CMYK Skin detection Human skin classification Face detection Face recognition 


  1. 1.
    Kovac, J., Peer, P., Solina, F.: Human skin color clustering for face detection. In: EUROCON 2003. Computer as a Tool. The IEEE Region 8, pp. 144–148. IEEE (2003)Google Scholar
  2. 2.
    Ibrahim, N.B., Selim, M.M., Zayed, H.H.: A dynamic skin detector based on face skin tone color. In: Proceedings of the 8th International Conference on INFOrmatics and Systems (INFOS2012) (2012)Google Scholar
  3. 3.
    Sobottka, K., Pitas, I.: A novel method for automatic face segmentation, face feature extraction and tracking. Signal Process. Image Commun. 12(3), 263–281 (1998)CrossRefGoogle Scholar
  4. 4.
    Mofaddel, M.A., Sadek, S.: Adult image content filtering: a statistical method based on multi-color skin modeling. In: 2nd International Conference on Computer Technology and Development (ICCTD), pp. 682–686 (2010)Google Scholar
  5. 5.
    Cheddad, A., Condell, J., Curran, K., Mc Kevitt, P.: A skin tone detection algorithm for an adaptive approach to steganography. Int. J. Signal Process. 89, 2465–2478 (2009)CrossRefGoogle Scholar
  6. 6.
    Jiann-Shu, L., Yung-Ming, K., Pau-choo, C.: The adult image identification based on online sampling. In: International Joint Conference on Neural Networks (IJCNN ‘06), pp. 2566–2571 (2006)Google Scholar
  7. 7.
    Vezhnevets, V., Sazonov, V., Andreeva, A.: A survey on pixel-based skin color detection techniques. In: Proceedings of Graphicon, Moscow, Russia, pp. 85–92 (2003)Google Scholar
  8. 8.
    Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin color modeling and detection methods. Pattern Recognit. 40, 1106–1122 (2007)CrossRefGoogle Scholar
  9. 9.
    Al-Mohair, H.K., Mohamad-Saleh, J., Suandi, S.A.: Human skin color detection: a review on neural network perspective. Int. J. Innov. Comput. Inf. Control (ICIC) 8(12), 8115–8131 (2012)Google Scholar
  10. 10.
    Rahman, N.A., Wei, K.C., See, J.: RGB-H-CbCr skin color model for human face detection. In: Proceedings of the MMU International Symposium on Information & Communications Technologies (2006)Google Scholar
  11. 11.
    Menser, B., Wien, M.: Segmentation and tracking of facial regions in color image sequences. In: Proceedings of the SPIE Visual Communications and Image Processing, pp. 731–740 (2000)Google Scholar
  12. 12.
    Lee, J.Y., Yoo, S.I.: An elliptical boundary model for skin color detection. Proc. Int. Conf. Imaging Sci. Syst. Technol. (2002)Google Scholar
  13. 13.
    Al-Mohair, H.K., Mohamad-Saleh, J., Suandi, S.A.: Hybrid human skin detection using neural network and K-means clustering technique. Appl. Soft Comput. 33, 337–347 (2015)CrossRefGoogle Scholar
  14. 14.
    Osman, M.Z., Maarof, M.A., Rohani, M.F.: Towards integrating statistical color features for human skin detection. In: 18th International Conference on Engineering and Applied Sciences (ICEAS), Kuala Lumpur, vol. 18, no. 2 IV, pp. 627–631 (2016)Google Scholar
  15. 15.
    Doukim, C.A., et al.: Combining neural networks for skin detection. Int. J. Signal Image Process. (SIPIJ) 1(2), 1–11 (2011)Google Scholar
  16. 16.
    Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-color modeling and detection methods. Pattern Recognit. 40(3), 1106–1122 (2007)CrossRefGoogle Scholar
  17. 17.
    Sigal, L., Sclaroff, S., and Athitsos, V.: Estimation and prediction of evolving color distributions for skin segmentation under varying illumination. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2, 152–159 (2000)Google Scholar
  18. 18.
    Mahmoud, T.M.: A new fast skin color detection technique. World Acad. Sci. Eng. Technol. 43, 501–505 (2008)Google Scholar
  19. 19.
    Phung, S.L., Bouzerdoum, A., Chai, D.: A novel skin color model in YCbCr colour space and its application to human face detection. In: International Conference on Image Processing (ICIP’2002), vol. 1, pp. 289–292 (2002)Google Scholar
  20. 20.
    Hsu, C.L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 696–706 (2002)CrossRefGoogle Scholar
  21. 21.
    Khan, R., Hanbury, A., Stottinger, J., Bias, A.: Color based skin classification. Pattern Recognit. Lett. 33(2), 157–163 (2012)CrossRefGoogle Scholar
  22. 22.
    Phung, S.L., Chai, D., -Bouzerdoum, A.: A novel skin color model in YCbCr color space and its application to human face detection. Proc. IEEE Int. Conf. Image Process. 1, I-289–I-292 (2002)Google Scholar
  23. 23.
    Dariusz, J.S., Weronika, M.: Human colour skin detection in CMYK colour space. IET Image Process. 9(9), 751–757 (2015)CrossRefGoogle Scholar
  24. 24.
    Tayal, Y., Lamba, R., Padhee, S.: Automatic face detection using color based segmentation. Int. J. Sci. Res. Publ. 2(6), 1–7 (2012)Google Scholar
  25. 25.
    Atharifard, A., Ghofrani, S.: Component-based face detection in color images. World Appl. Sci. J. 13(4), 847–857 (2011)Google Scholar
  26. 26.
    Bin Ghazali, K.H., Ma, J., Xiao, R.: An innovative face detection based on skin color segmentation. Int. J. Comput. Appl. 34(2), 6–10 (2011)Google Scholar
  27. 27.
    Anukrishnan, N., Ramya, B., Mohan, S.: Design and development of car ignition access control system based on face recognition technique. SAS TECH J. 9(2), 63–70 (2010)Google Scholar
  28. 28.
    Samart, N., Chiechanwattana, S., Sunat, K.: A novel rule for face region detection based on RGB-HSV-YCbCr skin model. In: 3rd International Conference on Science and Technology for Sustainable Development of the Greater Mekong Sub-region, vol. 2, no. 1, pp. 330–337 (2011)Google Scholar
  29. 29.
    Chaves-González, J.M., Vega-Rodríguez, M., Gómez-Pulido, J., Sánchez-Pérez, J.M.: Detecting skin in face recognition systems: a colour spaces study. Digit. Signal Process. 20(3), 806–823 (2010)CrossRefGoogle Scholar
  30. 30.
    Frode, E.S., Levent, N., Yo-Ping, H.: Simple and practical skin detection with static RGB Color lookup tables: a visualization-based study. IEEE Int. Conf. Syst. Man Cybern. SMC 2371–2375 (2016)Google Scholar
  31. 31.
    Kawulok, M., Kawulok, J., Nalepa, J., Smolka, B.: Self-adaptive algorithm for segmenting skin regions. EURASIP J. Adv. Signal Process. (170) (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and ApplicationsGandhigram Rural Institute – Deemed UniversityGandhigramIndia

Personalised recommendations