Abstract— In this paper a fuzzy approach for the classification of skin color tones in color images is presented. The paper is divided into two stages. The first stage consists of the selection of images that contained human faces of different skin color tones. A subset made up of these images was submitted to the opinion of a group of people with the aim of classifying them into their respective skin color tones: Black, Brown, and White. In the second stage of the paper, the information obtained from the research carried out, jointly with the study of the colors and their defining tones in relation to the RGB color system, were used for the definition of the fuzzy sets as well as the inference rules implemented into the system. In this manner, the developed system is able to classify a determined color into a possible skin color. I. INTRODUCTION attern recognition is a classic problem in computer vision with various practical applications associated to it. Color images increase the complexity of pattern recognition, once external factors, such as light, shade, etc, have harmed the standard recognition in an image. The automatic localization of facial regions is an important initial process for human face detection or recognition systems. The detection of human faces in uncontrolled environments is a complex problem, as it involves many variables that influence recognition such as, glasses, beards, shade and shadows, occlusion, etc. This problem has been a research topic within various areas of image processing and visual computation. A reliable skin segmentation system could minimize the complexity and increase the performance as well as the usability of facial image recognition systems. [3]. The objective of the classification of a pixel into "skin color" is to determine if the color of a pixel is or is not a skin color. A good "skin color" classifier should take into consideration all different skin types (White, Brown, black, yellow, etc) as well as the environmental factors that influence in the color of an image, such as illumination [4]. Manuscript received March, 15, 2006. I. A. G. Boaventura is with the Department of Computer Science and Statistic, University of State of São Paulo, São José do Rio Preto, SP 15054000 Brazil (corresponding author to provide phone: 55-17-3221-2205; fax: 55-17-3221-2203; e-mail: ines@ ibilce.unesp.br). V. M. Volpe is with UNIRP – Centro Universitário de Rio Preto, São José do Rio Preto, SP, Brazil (e-mail: vmvolpe@unirpnet.com.br). I. N. da Silva is with the Electrical Engineering Department, University of São Paulo, São Carlos, SP (e-mail: insilva@sel.eesc.usp.br ). A. Gonzaga is with the Electrical Engineering Department, University of São Paulo, São Carlos, SP (e-mail: agonzaga@sc.usp.br) . The detection of skin is a complex process, with a high degree of uncertainty attached to it, as well as being subject to external factors. The definition of skin color, when dealing with a digital representation, becomes complex as besides environmental influences connected to the place where the photo was taken, there also exist different skin characteristics in relation to a peoples’ geographical location, as well as skin tone variations depending on the individual’s race. The term "skin color" is also in itself a subjective idea, especially when based on the point of view of human interpretation. In this sense, the idea of "skin color" takes on an imprecise and vague definition, thus making the use of fuzzy logic an appropriate modeling tool. This paper has as objective to classify pixel colors into "skin color" tones. The images should be separated into regions where color is the separating characteristic. These regions will be classified where possible as "skin color" and, if there is positive confirmation a skin color tone is applied to that region. II. PROBLEM DEFINITION The problem when mapping a skin color consists of identifying a continuous irregular distribution (mathematically complex). A skin color captured by the human eye or by some type of photosensitive equipment, depends strongly on the quantity of illumination. Therefore, the problem being dealt with is to carry out this mapping through the use of a fuzzy system to verify if determined pixel regions meet on the color strip that represents skin colors and, besides, this supply a classification of skin tones for these regions In working with colors in a general sense, there exist various questions that need to be taken into consideration. In a natural scene, the colors of the objects and the illumination are neither restricted nor controlled. Texture deformations such as shade and shadows, occlusions, light variations along with other problems, make the segmentation of an image difficult. This difficulty can be better understood by representing the colors in the image through the use of an adequate representative space such as RGB, HSV or any other such space. Due to the nature of the scene’s characteristics, the colors are seen within these spaces as a "cloud" formation of diverse configurations, some being lightly scattered while others denser, and some presenting a large variation in the value of the perceived color. This is also something that occurs with "skin color". In relation to the representation of the "skin color" class within the color spaces, a very incisive recent result made a statement as to the best choice of color space for the detection of skin [5]. As a general conclusion, the authors suggest that the best spaces are YCbCr and RGB when dealing with separability, besides this, the RGB space is ranked in first or second place in the lists out of five of the eight performance Fuzzy Classification of Human Skin Color in Color Images I. A. G. Boaventura, V. M. Volpe, I. N. da Silva, A. Gonzaga P