Application of Feature based Face Detection in Adaptive Skin Pixel Identification Using Signal Processing Techniques

Abstract

Changes in illumination can substantially impact the apparent color of the skin, jeopardizing the effectiveness of any color-based segmentation method. Our solution to this problem is to use adaptive technology to generate skin color models in real-time. We employ a Viola-Jones feature-based face detector built-in MATLAB to sample faces inside a picture in a moderate-recall, high-precision configuration. We extract a set of pixels that are likely to be from skin areas from these samples. Then, filter them based on their relative luma values to remove non-skin face characteristics, producing a set of pixels. We train a unimodal Gaussian function to model the skin color in the provided image in the normalized rg color space using this representative set–a combination of the modeling strategy and color space that aids us in various ways. Subsequently, a developed function is employed for each pixel in the picture, allowing the likelihood that each pixel represents skin to be calculated. Application of a binary threshold to the computed probabilities may used to segment the skin. We discuss various current techniques in this work, detail the methodology behind our new proposed model. Moreover, provide the outcomes of its application to random photos of individuals with recognizable faces, which we found to be quite encouraging, and explores its possibilities for usage in real-time systems.