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Skin Color Detection Using Region-Based Approach
Rudra PK Poudel, Jian J Zhang, David Liu, Hammadi Nait-Chairf
Pages - 385 - 394     |    Revised - 15-08-2013     |    Published - 15-09-2013
Volume - 7   Issue - 4    |    Publication Date - September 2013  Table of Contents
Skin Color Detection, Bayes Classifier, Superpixels, MRF.
Skin color provides a powerful cue for complex computer vision applications. Although skin color detection has been an active research area for decades, the mainstream technology is based on the individual pixels. This paper, which extended our previous work [1], presented a new region- based technique for skin color detection which outperformed the current state-of-the-art pixel- based skin color detection technique on the popular Compaq dataset [2]. Color and spatial distance based clustering technique is used to extract the regions from the images, also known as superpixels followed by a state-of-the-art non-parametric pixel-based skin color classifier called the basic skin color classifier. The pixel-based skin color evidence is then aggregated to classify the superpixels. Finally, the Conditional Random Field (CRF) is applied to further improve the results. As CRF operates over superpixels, the computational overhead is minimal. Our technique achieved 91.17% true positive rate with 13.12% false negative rate on the Compaq dataset tested over approximately 14,000 web images.
CITED BY (3)  
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Dr. Rudra PK Poudel
Media School, Bournemouth University Poole, BH12 5BB - United Kingdom
Professor Jian J Zhang
Media School, Bournemouth University Poole, BH12 5BB - United Kingdom
Dr. David Liu
Siemens Corporate Research 755 College Road East, Princeton, NJ 08540 - United States of America
Associate Professor Hammadi Nait-Chairf
Media School, Bournemouth University Poole, BH12 5BB - United Kingdom