Abstract
In this paper a new skin color segmentation method that exploits pixels color space information is presented. We evaluate the discrimination strength of features extracted from the RGB and HSV color space and also of a new descriptor generated by combining both spaces. To facilitate our experimental evaluation we have used a linear SVM classifier since it provides certain advantages in terms of computational efficiency compared with its kernel based counterparts. Experiments conducted in video sequences depicting subjects eating and drinking, recorded in complex indoor background and different lightning conditions, where the developed methods achieved satisfactory skin color segmentation.
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Marami, E., Tefas, A., Pitas, I. (2011). Nutrition Assistance Based on Skin Color Segmentation and Support Vector Machines. In: Czachórski, T., Kozielski, S., Stańczyk, U. (eds) Man-Machine Interactions 2. Advances in Intelligent and Soft Computing, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23169-8_20
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DOI: https://doi.org/10.1007/978-3-642-23169-8_20
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