| Summary: | In this paper we have used a simple and efficient color-based approach to segment human skin pixels from complex background, using a 2-D histogram-based approach. For skin segmentation, a total of 446,007 skin samples from the training set is manually cropped from the RGB color images, to calculate three lookup tables based on the relationship between each pair of the triple components R, G, B. Derivation of skin classifier rules from the lookup tables are based on how often each attribute value (interval) occurs, and their associated certainty values. One of the simplest features used for the human face detection problem is the skin color information. A simple and relatively efficient histogram-based algorithm to segment skin pixels from a complex background is presented. The histogram-based algorithm used here is referred to as the lookup table (LUT) and is adopted to identify those intervals which may fall in the skin locus plane. For that purpose, a total of 306,401 skin samples are manually collected from RGB color images to calculate three lookup tables based on the relationship between each single pair of tlie three components (R, G, B). To estimate the skin locus boundary, a skin classifier box is created by integration of the proposed three heuristic rules based on how often each RGB pixel-relationship falls into its interval.
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