# Image Segmentation for Human Skin Detection
## 3 Skin Detection
### Challenges  
Considering the classification of pixels in an image as skin or non-skin as a typical image segmentation problem, the adopted approach must have the ability to overcome some difficulties (low illumination, camera characteristics, complex background, subject movement, etc.) [5]. Depending on the expected result for the final application of the skin identification process, some challenges may substantially affect this result. The following are the main ones:  
1. Lighting conditions: The illumination applied at the moment of image capture directly impacts the result, especially in approaches that use color space analysis to determine if the pixel belongs (or not) to a certain region classified as skin. The lighting variation in the environment where the image was captured creates one of the most difficult issues for this operation. Computer vision must have the ability to adjust to this variation, recognizing the same object in different lighting conditions in the same way that human vision can.
2. Ethnicity: Ethnic skin tone features represent a difficulty during the classification process because the enlargement of the skin region causes an increase in the occurrence of false positives. Some approaches adopt as a measure to overcome this difficulty the elaboration of a skin pixel model based on the previous detection of the face of the image object, and from the resulting map, they classify the image pixels as skin or non-skin. Although this method can work around some cases of skin tone variation, it has no efficient application when there is no face in the image and when there are several people in the same image. Another factor that may make this technique unfeasible is the increased computational consumption as well as the increased processing time.
3. Background: The accuracy of skin detection, in the process of segmentation, is severely affected when the background is complex and contains textures and colors similar to the skin region in the color space. The increase in false positives makes skin detection in certain images impractical. Some authors consider this situation a probabilistic problem and propose the definition of a skin probability map for each pixel of an image. Although the result can significantly reduce the occurrence of false positives, there is an increase in the need for computational power, which can make the approach unfeasible for certain applications.
4. Characteristics of the scanned image: Applications in which the scanned images have variable characteristics impact the result obtained. Images that have multiple objects, small objects, and objects from different perspectives represent increased difficulty in the segmentation process. The process needs to have the ability to detect skin without being able to use features such as skin texture. In computer vision, the ability to detect an object is usually related to a set of preknown characteristics of the object, such as size, position, and quantity, thus generating a certain restriction for the result. It happens that in some final application demands, the condition of the objects in the image is not known, and the computer vision system needs to have the same capacity as human vision to obtain the result, waiting independently of the characteristics of the objects in the examined image.