SkinColorSegmentationinFuzzyYCBCRColor Space with the Mamdani Inference 136 As Figure 5 shows membership degree related to being non skin color of this pixel is more than it's member degree of being skin color .so the system recognized that how much is color skin and how much is non color skin. Below( Figure 6 ) are some images which the above method has been applied on them: Figure 6: Left :original image, right: system's result Conclusion In this method for segmentation of color skin , in addition to considering the features of color intensity, brightness component also considered therefore in different light environments easily not-skin and skin colors are identified and separated.In many applications, knowing that how much the color of a pixel acts like human's skin color is important in our algorithm easily and with high precision decision making for this kind of problems is possible.Also in regions where skin color with non-skin color are overlapped , using the membership degree of fuzzy's criteria, can accurately determine being the skin color of a pixel or being non-color skin. Acknowledgment This work received support from Department of Computer Engineering, Islamic Azad University, sari Branch and the Fakhredin Asad Gorgani,non-profit and private institute of higher education.
137 Mohammad Saber Iraji and Ali Yavari References  Chiunhsiun Lin , Face detection in complicated backgrounds and different llumination conditions by using YCbCr color space and neural network , National Taipei University, Taipei 10433, Taiwan, ROC.,  Chia-Feng Juang and Shen-Jie Shiu , Using self-organizing fuzzy network with support vector learning for face detection in color images ,Department of Electrical Engineering, National Chung-Hsing University, Taichung 402, Taiwan, ROC.  Charles Poynton, Digital Video and HDTV, Chapter 24, pp. 291–292, Morgan Kaufman, 2003.  Garcia, C., Tziritas, G., 1999. Face detection using quantized skin color regions merging and wavelet packet analysis. IEEE Trans. Multimedia 1 (3), 264–277.  Hsu, Rein-Lien, Abdel-Mottaleb, M., Jain, A.K., 2002. Face detection in color images. IEEE Trans. Pattern Anal. Machine Intell. 24 (5), 696–706.  Hideki Nodaa, Michiharu Niimia ,Department of Systems Innovation and Informatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Japan, 2007.  Jones, M., Rehg, J.M., 1998. Statistical Color Models with Application to Skin Detection, Technical Report Series, Cambridge Research Laboratory,December 1998.  Jose M. Chaves-González, Miguel A. Vega-Rodriguez, Juan A. Gomez-PulÍdo,Juan M. Sánchez-Pérez, Univ.Extremadura,Dept. Technologies of Computers and Communications, Escuela Politécnica, Campus Universitario s/n, 10071, Cáceres, Spain. 2009  J. Yen, Fuzzy logic–a modern perspective, IEEE Transactions on Knowledge and Data Engineering 11 (1) (1989) 153–165.  Menser, B., Brunig, M., 1999. Locating human faces in color images with complex background. Intelligent Signal Process. Commun. Systems, 533–536, December.  Saber, E., Tekalp, A.M., 1998. Frontal-view face detection and facial feature extraction using color, shape and symmetry based cost functions. Pattern Recognition Lett. 19, 669–680.  Sobottka, K., Pitas, A., 1998. Novel method for automatic face segmentation, facial feature extraction and tracking. Signal Process. Image Commun. 12, 263–281.  S.N. Sivanandum, S. Sumathi, S.N. Deepa, Introduction to Fuzzy Logic Using MATLAB, Springer-Verlag, Berlin/Heidelberg, 2007.  Wei-Yuan Cheng, Chia-Feng Juang , An incremental support vector machine-trained TS-type fuzzy system for online classification problems , Department of Electrical Engineering, National Chung-Hsing University, Taichung 402, Taiwan, ROC.