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Skin Color Segmentation by Texture Feature Extraction and K-mean Clustering

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2 Author(s)
Pan Ng ; Dept. of Comput. & Inf. Sci., Univ. of Macau, Macau, China ; Chi-Man Pun

Skin Segmentation plays an important role in many computer vision applications. The aim of skin segmentation is to isolate skin regions in unconstrained input images. In this paper, a skin color segmentation approach by texture feature extraction and k-meaning clustering is proposed. We improved the traditional skin classification by combining both color and texture features for skin segmentation. After the color segmentation using a 16 - Gaussian Mixture Models classifier, the texture features are extracted using effective wavelet transform with a 2-D Daubechies Wavelet and represented as a list of Shannon entropy. The non-skin regions can be eliminated by the Skin Texture-cluster Elimination using K-mean clustering. Experimental results based on common datasets show that our proposed can achieve better performance of the existing methods with true positive of 93.8% and with false positives 28.4%.

Published in:

Computational Intelligence, Communication Systems and Networks (CICSyN), 2011 Third International Conference on

Date of Conference:

26-28 July 2011