# Image Segmentation for Human Skin Detection
## 3 Skin Detection
### Color Space  
Skin detection can be considered a binary pixel classifier with two classes: skin and non-skin [31]. The classification achievement depends on an appropriate feature set for capturing the essential elements of these pixels [8]. Each application needs the appropriate color model (RGB, HSV, or YCbCr, among others) [10] for when we consider the accuracy, the computational cost required for transformation, and the separability of the pixels with skin colors as the principal decision point [8]. Colorimetry, computer graphics, and video signal transmission standards have given rise to many color spaces with different properties. We have observed that skin colors differ more in luminance intensity than in chrominance due to lighting variation. Therefore, it is common to apply a linear or nonlinear transform in the RGB color space. This process changes the input's original space into another color space with independent components, eliminates in the classification process the luminance component analysis, and preserves the chrominance components [6]. A color space is made up of two components--chrominance and luminance [6]--and can be subdivided into four groups [31; 32]:  
1. Basic--RGB, CIE-XYZ: The RGB color model consists of three color channels, where \(R\) is red, \(G\) is green, and \(B\) is blue. Each channel has a value ranging from 0 to 255. This color space was originally developed for old cathode-ray tube (CRT) [32] monitors. Due to the model mixing the luminance and chromatic components, it is not always the most suitable color model for classifying pixels as skin and non-skin, as the variation in illumination greatly affects the accuracy of the result obtained from the classifier. The Commission Internationale de l'Eclairage (CIE) system describes color as a luminance component Y and two additional components \(X\) and \(Z\). According to [PERSON] et al. [6] the CIE-XYZ values were constructed from psychophysical experiments and correspond to the color matching characteristics of the human visual system.
2. Orthogonal--YCbCr, YDbDr, YPbPr, YUV, YIQ: The common goal among these color models is to represent the components as independently as possible, reducing redundancy between their channels, unlike in basic models [4; 32]. The luminance and chrominance components are explicitly separated, favoring their use in skin detection applications and, in this case, discarding the luminance component [6].
3. Perceptive--HSI, HSV, HSL, TSL: The RGB color space does not directly describe the perceptual features of color, such as hue (\(H\)), saturation (\(S\)), and intensity (\(I\)), and many nonlinear transformations are proposed to map RGB to perceptual features. The HSV space defines the property of a color that varies in the passage from red to green as the hue, the property of a color that varies in the passage from red to pink as the saturation, and the property that varies in the passage from black to white as intensity, brightness, luminance, or value [6]. HSV can be a very good choice for skin detection methods because the transformation from RGB to HSV is invariant to high intensities in white lights, ambient light, and surface orientations relative to the light source.
4. Uniforms--CIE-Lab, CIE-Luv: According to [6], perceptual uniformity describes how two colors differ in appearance to a human observer. However, perceptual uniformity in these color spaces is obtained at the expense of heavy computational transformations. In these color spaces, the calculations for the luminance (\(L\)) and chromaticity (ab or uv) are obtained by a nonlinear method of mapping the coordinates \(XYZ\).