Improved Skin Detection Using Colour Space and Texture
Commenced in January 2007
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Edition: International
Paper Count: 33090
Improved Skin Detection Using Colour Space and Texture

Authors: Medjram Sofiane, Babahenini Mohamed Chaouki, Mohamed Benali Yamina

Abstract:

Skin detection is an important task for computer vision systems. A good method of skin detection means a good and successful result of the system. The colour is a good descriptor for image segmentation and classification; it allows detecting skin colour in the images. The lighting changes and the objects that have a colour similar than skin colour make the operation of skin detection difficult. In this paper, we proposed a method using the YCbCr colour space for skin detection and lighting effects elimination, then we use the information of texture to eliminate the false regions detected by the YCbCr skin model.

Keywords: Skin detection, YCbCr, GLCM, Texture, Human skin.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1337609

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References:


[1] D. Ghimire and J. Lee. “A Robust Face Detection Method Based on Skin Color and Edges”. J Inf Process Syst, Vol.9, No.1, March 2013.
[2] Hyung-Soo lee, D.kim, Sang-Youn lee. “Robust face-tracking using skin color and facial shape”. AVBPA 2003 Springer.
[3] M. Sharma and S. Verma and A S Mandal. “Block Based Skin Color Detection for Automated Video Surveillance System”. International Journal of Scientific & Engineering Research, Volume 3, Issue 11, November-2012.
[4] Phung, S.L., Bouzerdoum, A., Chai, D. “Skin segmentation using color pixel classification: analysis and comparison”. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(1), 148–154 (2005).
[5] K. Kakumanu and S. Makrogiannis and N. Bourbakis: “A survey of skin color modeling and detection methods”. Pattern Recognition 40(3), 1106–1122 (2007).
[6] B. Khanal and D. Sidibé. “Efficient Skin Detection under Severe Illumination Changes and Shadows”. Springer. ICIRA 2011, Part II, LNAI 7102, pp. 609–618.
[7] L.Tang and H. Tang. “Image Sharpening and Denoising Method Based on Anisotropic Diffusion Model”. Springer. AISC 62, pp. 313-320.2009.
[8] D. Gadkari. “Image quality analysis using glcm”. Dhanashree gadkari. “Image quality analysis using glcm”. University of pune, 2000.