Skin Colour Segmentation Using Finite Bivariate Pearsonian Type-Iib Mixture Model and K-Means

Abstract

Skin colour segmentation plays an important role in computer vision, face detection and human related systems. Much work has been reported in literature regarding skin colour detection using Gaussian mixture model. The Gaussian mixture model has certain limitations regarding the assumptions like pixels in each component are mesokurtic, having negative range and it doesn't adequately represent the variance of the skin distribution under illumination conditions. In this paper we develop and analyze a new skin colour segmentation based on HSI colour space using bivariate Pearsonian type-IIb mixture model. The model parameters are estimated by deriving the updated equation of EM-Algorithm. The initialization of the model parameters is done through K-means algorithm and method of moments. The segmentation algorithm is obtained using component maximum likelihood under Bayes frame. The experimental results using hue and saturation as feature vector revealed that the developed method perform better with respect to segmentation performance metrics than that of Gaussian mixture model. This method is useful in face detection and medical diagnostics. KEYWORDS: Skin segmentation, bivariate Pearsonian type-IIb mixture model, EM-Algorithm, HSI Colour space.