Scheme fuzzy approach to classify skin tonalities through geographic distribution


One of the most significant current discussions in Computer Science is the skin recognition. Many papers have studied the skin detection using different techniques, artificial vision, machine learning, deep learning among others. Despite its long success, the skin recognition has several problems in use. However, there has been little discussion about generate a skin tonalities classification. The aim of this paper is to propose a system to skin tonalities through geographic distribution based on clustering algorithms, pattern recognition and fuzzy logic. This distribution gives us the opportunity to classify the skin tonalities. We can study each skin tonality for any applications as medical diagnosis, security. In the first stage, we use the RGB color model to training the system. Then, we tested the system with different color models. We use color model with the best result to propose geographic distribution based skin tonalities. The results show that is possible to generate a skin tonalities classification. The proposed system is using to skin recognition, showing interesting results under controlled and no controlled conditions.

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The authors would like to thank to the National Science and Technology Council of Mexico for the financial support during the realization of this research. Andres Hernandez-Matamoros appreciates the support received from Diana Matamoros, Luis Hernandez, and Francisco Matamoros.

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Correspondence to Hamido Fujita.

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Hernandez-Matamoros, A., Fujita, H., Nakano-Miyatake, M. et al. Scheme fuzzy approach to classify skin tonalities through geographic distribution. J Ambient Intell Human Comput 11, 2859–2870 (2020).

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  • Skin
  • Color model
  • Fuzzy logic
  • Geographic distribution