Simple and practical skin detection with static RGB-color lookup tables: A visualization-based study

Abstract

Many skin detection approaches have been proposed in the image analysis literature. Some are simple and static; the others are dynamic and rely on complex machine learning algorithms and training data. Generally the simple approaches are preferred. We hypothesize that the developers' choice for the simple approaches are due to the reasonable quality of results and ease of implementation, since the results of more sophisticated results are not readily available. This paper explores the skin color of a large number of hand samples using color space visualization. The results suggest that a static method may suffice for many applications, but that a small set of rules is not enough to capture the details of skin. Moreover, the results suggest that successful skin detection does not depend on the color space used as there are no apparent advantages of using a perceptual uniform color space such as CIElab. A skin detection approach based on RGB-color table-lookup is proposed that is able to capture the complex skin color cluster shape. The method is practical and simple to implement with minimal computational cost. The lookup table is released into the public domain.