# Image Segmentation for Human Skin Detection
## 3 Skin Detection  
### Segmentation  
Most approaches on the subject of skin segmentation are based on a particular color space [2; 4; 21; 23] or on fusion components from different color spaces [3; 7; 24], but they are evidently limited to color images only. In general, skin color occupies a limited range of values in different color spaces [25], and detection consists of determining an optimal model for the thresholds belonging to the skin and non-skin groups [7]. In addition, some studies, such as [24; 25; 26], still tried to analyze other features, such as by segmenting regions by texture type. However, this approach needs images with previously known features, because it is necessary to locate the descriptors corresponding to the textures, which need to be preserved. A skin detection process mainly needs two decisions, as presented in Figure 5. The first choice is to determine the appropriate color space in which it is easiest to discriminate skin pixels, which is a key factor in the design of a skin detector, considering the computational cost of transforming between color spaces [27]. The second decision is to determine the skin segmentation model that will be used to classify a given pixel, being either skin or non-skin. Thus, the system must be intelligent and flexible enough to handle various aspects, such as different skin tones or lighting conditions, reducing false negative rates by not identifying a skin pixel as a non-skin pixel [8]. In addition, it should be sensitive enough to differences between classes--background objects with skin color--to reduce false positive rates (i.e., identifying a non-skin pixel as a skin pixel) [25]. Figure 4: Example of segmentation of human body parts: (**a**) input image, (**b**) face, (**c**) arm, (**d**) torso, and (**e**) leg. These choices aim to overcome the main challenges of skin recognition: the definition of an optimal model of skin and non-skin pixels for different skin types [26] and diverse lighting conditions [6; 8; 10]. Furthermore, the accuracy of skin recognition is affected by pixels of skin-like colors in the image background or clothing. These pixels generate false positives and hinder accurate skin recognition. This problem can be emphasized when we apply the recognition process in small regions containing skin, such as a person's hands, since the treatment enforced to suppress false negatives can also suppress small true regions of skin [25]. An example is shown in Figure 6, where there are areas of clothing pixels that are confused with the values of regions belonging to skin tones in the color space. The main question in the recognition of skin and non-skin pixels is to determine an optimal model for this classification, neither being rigid in the threshold values for skin tones nor being flexible, which would increase the various false positives [10; 20]. In general, the proposed approaches seek to balance recognition accuracy and the required computational power, since human-computer interaction applications need low computational costs coupled with high accuracy [3; 4]. The most common strategies for defining skin models can be separated into three approaches: (1) explicitly defined regions [2; 3], where the boundaries are fixed by a set of rules, (2) parametric methods [8; 28] and non-parametric methods [10; 24], which are based on histograms applied (or not) to Gaussian models and elliptical boundaries, and (3) machine learning-based approaches which, through the training of a suitable neural network model, enable the identification of skin and non-skin pixels [29]. According to [PERSON] and [PERSON] [30], the fundamental problem of segmentation is the division of an image into regions that satisfy certain conditions set to achieve the end goal. In skin segmentation, the problem becomes more complex because the goal is not exactly the detection of an object. It involves examination of image features, such as color regions of a given color space in color images and the intensity levels in non-color images.