Research Article Open Access

Statistical Pattern Recognition for Thresholding between Human Skin and Background in Color Images

Rafael Divino Ferreira Feitosa1, Vinícius Araújo Santos2, Leandro Luís Galdino de Oliveira2, Díbio Leandro Borges3, Lucas Calabrez Pereyra2 and Adriano Soares de Oliveira Bailão1
  • 1 Federal Institute Goiano - IF Goiano, Brazil
  • 2 Federal University of Goiás - UFG, Brazil
  • 3 University of Brasilia - UnB, Brazil


Many research works based on the tone of human skin have been developed to locate and track the human body for the purpose of recognition in color images. With respect to other techniques, some advantages of face detection based on skin color are the smaller processing time, invariant angles of rotation and the performance in semi-occluded faces. In this study we present the results of a survey that investigated the performance of 4 supervised classifiers in skin detection. In order to maximize the generalization of the models, a training set containing samples of individuals of different ages and ethnicities was used. Experimental results showed that the best performance was achieved by using an ANN and the worst results were yielded by LDA. With the Naive Bayes, QDA and ANN algorithms, we showed that the white, black, yellow and brown tones of human skin are in a well-defined range of the RGB color spectrum determined by common characteristics. We also compiled 2798 skin samples for treatment and 305 images with their manually obtained labels as supplementary material, which was made available to help in the development of further research in human skin detection.

Journal of Computer Science
Volume 13 No. 2, 2017, 22-33


Submitted On: 7 March 2016 Published On: 10 April 2017

How to Cite: Feitosa, R. D. F., Santos, V. A., de Oliveira, L. L. G., Borges, D. L., Pereyra, L. C. & Bailão, A. S. O. (2017). Statistical Pattern Recognition for Thresholding between Human Skin and Background in Color Images. Journal of Computer Science, 13(2), 22-33.

  • 0 Citations



  • Applied Computing
  • Machine Learning
  • Skin Detection