Suppressing False Nagatives in Skin Segmentation

  • Roziati Zainuddin
  • Sinan Naji
  • Jubair Al-Jaafar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6485)


Human skin segmentation in colored images is closely related to face detection and recognition systems as preliminary required step. False negative errors degrade segmentation accuracy and therefore considered as critical problem in image segmentation. A general innovative approach for human skin segmentation that substantially suppresses false negative errors has been developed. This approach employed multi-skin models using HSV color space. Four skin color clustering models were used, namely: standard-skin model, shadow-skin model, light-skin model, and redness-skin model. The color information was used to segment skin-like regions by transforming the 3-D color space to 2-D subspace. A rule-based classifier produces four skin-maps layers. Each layer reflects its skin model. Pixel-based segmentation and region-based segmentation approaches has been combined to enhance the accuracy. The inspiring results obtained show that the suppression of false negatives is substantial and leads to better detection and recognition.


Human skin segmentation skin color modelling face detection HSV 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Roziati Zainuddin
    • 1
  • Sinan Naji
    • 1
  • Jubair Al-Jaafar
    • 2
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.School of Computer TechnologySunway University CollegeKuala LumpurMalaysia

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