Region based elimination of noise pixels towards optimized classifier models for skin pixel detection

The extraction of the skin pixels in a human image and rejection of non-skin pixels is called the skin segmentation. Skin pixel detection is the process of extracting the skin pixels in a human image which is typically used as a pre-processing step to extract the face regions from human image. In past, there are several computer vision approaches and techniques have been developed for skin pixel detection. In the process of skin detection, given pixels are been transformed into an appropriate color space such as RGB, HSV etc. And then skin classifier model have been applied to label the pixel into skin or non-skin regions. Here in this research a “Region based elimination of noise pixels and performance analysis of classifier models for skin pixel detection applied on human images” would be performed which involve the process of image representation in color models, elimination of non-skin pixels in the image, and then pre-processing and cleansing of the collected data, feature selection of the human image and then building the model for classifier. In this research and implementation of skin pixels classifier models are proposed with their comparative performance analysis. The definition of the feature vector is simply the selection of skin pixels from the human image or stack of human images. The performance is evaluated by comparing and analysing skin colour segmentation algorithms. During the course of research implementation, efforts are iterative which help in selection of optimized skin classifier based on the machine learning algorithms and their performance analysis. Keywords-Digital Image Processing, Data Mining, Digital Image Processing, Skin Pixel Detection, Color