# Image Segmentation for Human Skin Detection
## 1 Introduction  
Due to the natural process of technological evolution, demands that require digital recognition from digital image processing and computer vision are increasingly appearing, such as people and movement identification and human skin recognition, among others. Of these, skin segmentation is an important step in enabling several computer vision-based applications, such as facial expression recognition [1], detection of nudity [2, 3] or child pornography [4], body motion tracking, gesture recognition [1], and skin disease diagnostics, among other human-computer interaction (HCI) applications [5]. Methods commonly used in the skin segmentation problem are, in general, situation- or application-specific [6, 7]. In the case of face recognition, the methods segment skin and non-skin pixels based on face detection [8], while in applications of medicinal interest (e.g. the abdominal region), they tend to solve the problem by actually considering the existence of elements corresponding to the abdomen in the examined image [9]. In these situations, the entire examination is applied in a controlled environment--the input image has previously known aspects, considering the lighting conditions, capture equipment, and objects present. There are approaches suggested by several authors which seek to segment the skin pixels according to each particular one in an optimal way [10, 11]. For example, in an application related to biometrics, the response time for the user is fundamental, together with an acceptable accuracy for the expected result of this application [12]. Otherwise, in an application with medical purposes, the processing speed tends to not represent so much relevance; otherwise, the accuracy becomes fundamental due to the characteristics of the analysis, which primarily aims to increase the accuracy of the result and not the prediction time. Therefore, considering the application and its requirements, one approach may be more suitable than another in segmenting skin and non-skin pixels. There are several problems--depending on the approach--when the input images have no given pattern, either because of difficulty in segmenting the elements belonging to the background of the objects of interest or the quality. The quality is affected by several factors, such as the image capture equipment, image illumination conditions during the capture process, and sampling process during digitization [6]. Therefore, this work aims to contribute to improving the image examination process in the forensic field, in which the input images may contain any of the above-mentioned aspects. Furthermore--considering the possibility of sharing forensic examination target material through internet usage applications--these images suffer from data compression, which causes the loss of relevant image information, reducing its quality [13]. During compression processing, quantization and sampling methods eliminate similar information from image pixels, severely affecting skin regions. Skin regions have very discrete pixel variations, and these form the texture of the skin. However, this texture ends up being compromised by the compression processing, removing from it the texture information [13]. Therefore, applications with a controlled environment, considering images with previously known quality standards, allow approaches and techniques based on skin texture recognition to present the expected results [7; 14]. However, these methods do not deliver the same results when examining images that have the skin texture information corrupted by the sampling and quantization process or the hard compression processing performed by sharing applications. Figure 1 presents the change of an image after sharing the image through the message exchange application. In this example, we can note there was a loss of a piece of the pixel information. Therefore, the input image was modified after the compression process.