A Robust Skin Colour Segmentation Using Bivariate Pearson Type IIαα (Bivariate Beta) Mixture Model

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B.N.Jagadesh 1,* K Srinivasa Rao 2 Ch. Satyanarayana 3

1. Department of Computer Science and Engineering Srinivasa Institute of Engineering & Technology NH-216, Cheyyeru, Amalapuram, A.P., INDIA

2. Department of Statistics Andhra University Visakhapatnam, A.P., INDIA

3. Department of Computer Science and Engineering Jawaharlal Nehru Technological University Kakinada Kakinada, A.P., INDIA.

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2012.11.01

Received: 4 Jul. 2012 / Revised: 10 Aug. 2012 / Accepted: 11 Sep. 2012 / Published: 8 Oct. 2012

Index Terms

Bivariate Pearson type II mixture model, skin colour segmentation, HSI colour space, segmentation quality metrics


Probability distributions formulate the basic framework for developing several segmentation algorithms. Among the various segmentation algorithms, skin colour segmentation is one of the most important algorithms for human computer interaction. Due to various random factors influencing the colour space, there does not exist a unique algorithm which serve the purpose of all images. In this paper a novel and new skin colour segmentation algorithms is proposed based on bivariate Pearson type II mixture model since the hue and saturation values always lies between 0 and 1. The bivariate feature vector of the human image is to be modeled with a Pearson type II mixture (bivariate Beta mixture) model. Using the EM Algorithm the model parameters are estimated. The segmentation algorithm is developed under Bayesian frame. Through experimentation the proposed skin colour segmentation algorithm performs better with respect to segmentation quality metrics such as PRI, VOI and GCE. The ROC curves plotted for the system also revealed that the proposed algorithm can segment the skin colour more effectively than the algorithm with Gaussian mixture model for some images.

Cite This Paper

B.N.Jagadesh,K.Srinivasa Rao,Ch.Satyanarayana,"A Robust Skin Colour Segmentation Using Bivariate Pearson Type IIαα (Bivariate Beta) Mixture Model", IJIGSP, vol.4, no.11, pp.1-8, 2012. DOI: 10.5815/ijigsp.2012.11.01


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