International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.4, No.11, Oct. 2012

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

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B.N.Jagadesh,K.Srinivasa Rao,Ch.Satyanarayana

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.


[1]Alexander Wong, Jacob Scharcanski and Paul Fieguth (2011), “Automatic Skin Lesion Segmentation via Iterative Stochastic Region Merging”, IEEE Trans. on Information Technology in Biomedicine, Vol.15, No.6,pp.929-936.

[2]Wei Ren Tan, Chee Seng Chan, Pratheepan Yogarajah, and Joan Condell (2012), “A Fusion Approach for Efficient Human Skin Detection”, IEEE Transactions on Industrial Informatics, Vol.8, No.1,pp.138-147.

[3]Chang-Yul Kim, Oh-Jin Kwon, and Seokrim Choi (2011), “A Practical System for Detecting Obscene Videos”,IEEE Transactions on Consumer Electronics, Vol.57, No.2, pp. 646-650.

[4]Fumihito Yasuma, Tomoo Mitsunaga, Daisuke Iso, and Shree K. Nayar (2010), “Generalized Assorted Pixel Camera: Postcapture Control of Resolution, Dynamic Range and Spectrum”, IEEE Transactions on Image Processing, Vol.19, No.9, pp.2241-2253.

[5]C. Chen, S.P. Chiang (1997), “Detection of human faces in colour images”, IEE Proc. Vision Image Signal Process, Vol.144 (6) pp.384–388.

[6]H. Wu, Q. Chen, M. Yachida (1999), “Face detection from color images using a fuzzy pattern matching method”, IEEE Trans. Pattern Anal.Mach. Intell. Vol. 21 (6) pp.557–563.

[7]M.H. Yang, N. Ahuja (1999), “Gaussian Mixture model for human skin color and its application in image and video databases”, Proceedings of SPIE: Conference on Storage and Retrieval for Image and Video Databases, Vol. 3656, pp. 458–466.

[8]L.M. Bergasa, M. Mazo, A. Gardel, M.A. Sotelo, L. Boquete (2000), “Unsupervised and adaptive Gaussian skin-color model”, Image Vision Comput. Vol.18 (12) pp.987–1003.

[9]D. Brown, I. Craw, J. Lewthwaite (2001), “A SOM based approach to skin detection with application in real time systems”, BMVC01.

[10]S. McKenna, S. Gong, Y. Raja (1998), “Modeling facial colour and identity with Gaussian mixtures”, Pattern Recognition Vol.31 (12) pp.1883–1892.

[11]J.C. Terillon, M. David, S. Akamatsu (1998), “Detection of human faces in complex scene images by use of a skin color model and of invariant Fourier–Mellinmoments”, ICPR98, pp. 350–1355.

[12]C. Garcia, G. Tziritas (1999), “Face detection using quantized skin color regions merging and wavelet packet analysis”, IEEE Trans. Multimedia Vol.1 (3) pp. 264–277.

[13]Y. Wang, B. Yuan (2001), “A novel approach for human face detection from color images under complex background”, Pattern Recognition Vol.34 (10) pp.1983–1992.

[14]Q. Zhu, K.-T. Cheng, C.-T. Wu, Y.-L. Wu (2004), “Adaptive learning of an accurate skin-color model”, AFGR04.

[15]Y. Dai, Y. Nakano (1996), “Face-texture model based on SGLD and its application in face detection in a color scene”, Pattern Recognition Vol. 29 (6) pp.1007–1017.

[16]D. Chai, K.N. Ngan (1998), “Locating facial region of a head-and-shoulders color image”, ICFGR98.

[17]D. Chai, A. Bouzerdoum (2000), “A Bayesian approach to skin color classification in YCbCr color space”, IEEE TENCON00, Vol. 2 pp. 421–424.

[18]F. Marques, V. Vilaplana (2000), “A morphological approach for segmentation and tracking of human face”, ICPR 2000.

[19]G. Gomez, M. Sanchez, L.E. Sucar (2002), “On selecting an appropriate colour space for skin detection”, Springer-Verlag: Lecture Notes in Artificial Intelligence, Vol. 2313, pp. 70–79.

[20]J.J. de Dios, N. Garcia, (2003), “Face detection based on a new color space YCgCr”, ICIP03.

[21]S. Kawato, J. Ohya,( 2000), “Automatic skin-color distribution extraction for face detection and tracking”, Fifth International Conference on Signal Processing, Vol. 2 pp. 1415–1418.

[22]G. Wyszecki, W.S. Stiles (1967), Color Science, Wiley, New York.

[23]G.V.S. Raj Kumar, K.Srinivasa Rao and P.Srinivasa Rao (2011), “Image Segmentation and Retrievals based on finite doubly truncated bivariate Gaussian mixture model and K-means”, International Journal of Computer Applications, Vol. 25. No.5 pp.5-13.

[24]Rafel C Gonzalez and Richard E Woods (2001), “Digital Image Processing”, Pearson education, India

[25]K.Srinivasa Rao, B.N.Jagadesh, Ch.Satyanarayana (2012), “Skin Colour Segmentation using Fintte Bivariate Pearsonian Type-IVa Mixture Model”, Computer Engineering and Intelligent Systems, Vol.3, No.5, pp.46-55.

[26]J. Cai, A. Goshtasby (1999), “Detecting human faces in color images”, Image and Vision Computing 18, pp.63-75.

[27]Hayit Greenspan, Jacob Goldberger, Itay Eshet (2001) “Mixture model for face color modeling and segmentation” Pattern Recognition Letters 22 (2001), pp.1525-1536.

[28]P.Kakumanu, S.Makrogiannis, N. Bourbakis (2007) “A survey of skin-color modeling and detection methods”, Pattern Recognition, vol.40, pp.1106-1122, 20.

[29]Ki-Won Byun and Ki-Gon Nam(2012), “Skin Region Detection Using a Mean Shift Algorithm Based on the Histogram Approximation”,Vol.13,No.1,pp.10-15.

[30]Jianyong Sun , Ata Kabán b, Jonathan M. Garibaldi(2010), “Robust mixture clustering using Pearson type VII distribution”, Pattern Recognition letters.

[31]Norrman L. Johnson, Samuel Kotz and Balakrishnan (2000), “Continuous Multivariate Distributions”, John Wiley and Sons Publications, New York.

[32]Mclanchlan G. and Krishnan T. (1997), “The EM Algorithm and Extensions” , John Wiley and Sons, New York -1997. 

[33]Mclanchlan G. and Peel D.(2000) “The EM Algorithm For Parameter Estimations”, John Wiley and Sons, New York.

[34]Rose H.Turi (2001), Cluster Based Image Segmentation, PhD Thesis, Monash University, Australia

[35]Unnikrishnan R., Pantofaru C., and Hernbert M. (2007), “Toward objective evaluation of image segmentation algorithms”, IEEE Trans.Pattern Annl.Mach.Intell, Vol.29 ,No.6, pp. 929-944.

[36]Meila M. (2005) Comparing Clustering – An axiomatic view, in proc.22nd Int. Conf. Machine Learning, pp. 577-584. 

[37]Martin D. Fowlkes C., Tal D. and Malik J. (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, in proc. 8th Int. Conference Computer vision, Vol.2 pp.416- 423.