Selection of skin Based Region-of-Interest (ROI) Using Clustering AbstractRegion of Interest (ROI) is defined as regions containing user defined objects of interest. ROI extraction is a vital phase for various image processing applications.
Extracting ROI from images has been very much challenging as it is the base for further image analysis, interpretation and classification. ROI varies for different purpose of aim. However, the identified region are widely used for various domain-skin detection, image background removal, detect object in image, hand gesture detection and so on. In this thesis, the main concentration is to defining region of interest from an image based on skin detection. To define region of interest of skin, clustering method was used. Skin detection can be used as a preprocessing step for several applications included but not limited to various Human Computer Interaction (HCI) tasks. However, skin detection is a challenging problem due to sparse variations of skin tone of human.
Skin tone can be confused with background color, attire color, ethnicity, individual characteristics-age, sex, body parts, makeup, hair color, presence of non-human objects and camera calibration. Besides that, lightning conditions also plays a vital role. For these reasons, sometimes skin pixel values are very similar with non-skin pixels, make it hard to discriminate skin only pixels. Researchers have been working tirelessly for an efficient skin detection method but those are not beyond limitations. Various approach including pixel wise threshold for various color spaces, segmentation, face and hand detection based approaches are proposed. But it still lacks from a method which can be applied for all types of skin detection. In this thesis, a novel skin detection method is proposed which has the following characteristics:• It is free from any manual threshold values.
Which makes it better choice for dealing uncertain conditions• The method is based on clustering, since skin pixels of a human are uniform in nature• Proposed approach automatically define number of clusters that is a bottleneck for unsupervised learning Chapter 01IntroductionSkin detection is perhaps the most widely used primitive in human image processing research. Skin detection mostly used as a primary step in various human concerned image processing applications. Skin detection is method of discriminating human skin pixel from non-skin pixels in an image or video . It is one of the prominent research area in human computer interaction, face detection, face tracking [2, 3], gesture recognition , computational health informatics, web content filtering and many more. Skin detection is used as a cue for detecting people in real life images. The main challenge is to make skin detection robust to the large variations in appearance that can occur. However, there are various factors that make skin detection challenging.
Among them variations in illumination, various ethnicity people with many skin tones, presence or absence of shadows in an image or videos, various background color and objects including wood, cloths and their similarity to skin, human hair with different variations and their resemblance to skin color, using makeup that changes the natural skin color and different camera characteristics make skin detection problem hard. Efficient handling of aforementioned challenges demands a model that is capable of differentiate skin and non-skin pixels. But until now that seems not to be achieved. In this thesis, a skin detection model is proposed which can overcome the challenges and perform better in real world skin detection problem. Researchers have been working tirelessly to find a technique which will be able to detect skin in spite of present challenges. However, Skin detection problem can be considered a binary classification problem, meaning, a pixel can be classified whether it is a skin pixel or not. There are mainly two types of skin detection, either pixel based or region based.
In pixel based detection, a pixel is classified compared with its neighbor either as a skin pixel or not. Skin detection that is based on various color spaces are an example for this type of detection. In other hand, region based skin detection focus on spatially arrangement of skin pixel with additional information of intensity and texture. However, Vezhnevets et al. , Kakumanu et al. , and Phung et al.  has conducted surveys about skin color modeling and skin segmentation based on color information in different color spaces.
Phung has discovered that skin detection accuracy does not depend on choice of color space or color quantization bin sizes. Besides for skin segmentation a few researchers have also used texture [8, 9, 10] or shape  information in combination with color-
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