Abstract
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.
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Zainuddin, R., Naji, S., Al-Jaafar, J. (2010). Suppressing False Nagatives in Skin Segmentation. In: Kim, Th., Lee, Yh., Kang, BH., Ślęzak, D. (eds) Future Generation Information Technology. FGIT 2010. Lecture Notes in Computer Science, vol 6485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17569-5_15
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DOI: https://doi.org/10.1007/978-3-642-17569-5_15
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