Improved Human Skin Segmentation Using Fuzzy Fusion Based on Optimized Thresholds by Genetic Algorithms

  • Anderson SantosEmail author
  • Jônatas Paiva
  • Claudio Toledo
  • Helio Pedrini


Human skin segmentation has several applications in computer vision beyond its main purpose of distinguishing between skin and nonskin regions. Despite the large number of methods available in the literature, accurate skin segmentation is still a challenging task. Many methods rely only on color information, which does not completely discriminate the image regions due to variations in lighting conditions and ambiguity between skin and background color. This chapter extends upon a self-contained method for skin segmentation that outlines regions from which the overall skin color can be estimated and such that the color model is adjusted to a particular image. This process is based on thresholds that were empirically defined in a first approach. The proposed method has three main contributions over the previous one. First, genetic algorithm (GA) is applied to search for better thresholds that will be used to extract appropriate seeds from the general probability and texture maps. Next, the GA is also applied to define thresholds for edge detectors aiming to improve edge connections. Finally, a fuzzy method for fusion is included where its parameters are optimized by GA during a learning phase. The improvements added to the skin segmentation method are evaluated on a set of hand gesture images. A statistical analysis is conducted over the computational results achieved by each evaluated method, indicating a superior performance of our novel skin segmentation method.


Human skin segmentation Fuzzy fusion Self-adaptation skin segmentation Genetic algorithms 



The authors are thankful to FAPESP (grant #2011/22749-8) and CNPq (grant #307113/2012-4) for their financial support.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Anderson Santos
    • 1
    Email author
  • Jônatas Paiva
    • 2
  • Claudio Toledo
    • 2
  • Helio Pedrini
    • 1
  1. 1.Institute of ComputingUniversity of CampinasCampinasBrazil
  2. 2.Institute of Mathematics and Computer ScienceUniversity of São PauloSão CarlosBrazil

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