A New Clustering-based Thresholding Method for Human Skin Segmentation Using HSV Color Space

Skin detection based on color can be applied in eHealth systems for preventive healthcare and computer-aided diagnosis. These algorithms could be incorporated in acquisition and preprocessing steps of the applications that assist with skincare, as prevention and detection of melanoma. In this paper we present the results of a study that investigated the reduction of the color spectrum in the HSV system for sample-based skin detection of individuals of different ages and ethnicities. The proposed HSV filter reduced the color spectrum by 97.4648{\%} so as to select candidates for human skin tones. It achieved low sensitivity (54.6333{\%}) and high specificity (92.6390{\%}) in human skin detection in color digital images when compared to the performance of other algorithms proposed in the literature. Different from other filters described in the literature which propose a single interval for human skin in the HSV system, this model presents and discusses 13 intervals in the possible spectrum which present a well-defined variation in terms of tone.