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A Bayesian skin/non-skin color classifier using non-parametric density estimation

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3 Author(s)
Chai, D. ; Sch. of Eng. & Math., Edith Cowan Univ., Perth, WA, Australia ; Phung, S.L. ; Bouzerdoum, A.

This paper addresses an image classification technique that uses a Bayesian decision rule for minimum cost to determine if a color pixel has skin or non-skin color. Our proposed approach employs non-parametric estimation of class-conditional probability density functions of skin and non-skin color with a feature vector that consists of all three components of the RGB color space. Experimental results demonstrate that the classifier can achieve good classification performance. Furthermore, its simplicity is an attractive feature for real-time applications. It is a useful tool for image processing tasks such as human face detection, facial expression and hand gesture analysis.

Published in:

Circuits and Systems, 2003. ISCAS '03. Proceedings of the 2003 International Symposium on  (Volume:2 )

Date of Conference:

25-28 May 2003

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