1 April 2008 Recall or precision-oriented strategies for binary classification of skin pixels
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J. of Electronic Imaging, 17(2), 023017 (2008). doi:10.1117/1.2916715
Abstract
Skin detection is a preliminary step in many applications. We analyze some of the most frequently cited binary skin classifiers based on explicit color cluster definition and present possible strategies to improve their performance. In particular, we demonstrate how this can be accomplished by using genetic algorithms to redefine the cluster boundaries. We also show that the fitness function can be tuned to favor either recall or precision in pixel classification. Some combining strategies are then proposed to further improve the performance of these binary classifiers in terms of recall or precision. Finally, we show that, whatever the method or the strategy employed, the performance can be enhanced by preprocessing the images with a white balance algorithm. All the experiments reported here have been run on a large and heterogeneous image database.
Francesca Gasparini, Silvia Corchs, Raimondo Schettini, "Recall or precision-oriented strategies for binary classification of skin pixels," Journal of Electronic Imaging 17(2), 023017 (1 April 2008). http://dx.doi.org/10.1117/1.2916715
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KEYWORDS
Skin

RGB color model

Binary data

Databases

Chromium

Data modeling

Light sources and illumination

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