Hybrid adaptation for detecting skin in color images
Issue title: Intelligent Computing for Pattern Recognition, Image Processing and Computer Vision
Papers from CIARP 2014, November 2-5, 2014, Puerto Vallarta, Jalisco, Mexico
Affiliations: Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland
Correspondence:
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Corresponding author: Michal Kawulok, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland. E-mail:[email protected]
Abstract: It has been reported in many works on skin detection and segmentation from color images that skin color models suffer from low specificity and high variance of the skin color, and this problem can be addressed by conforming the skin model to a presented scene. Here, we introduce a new hybrid adaptation system which combines two strategies, namely (i) adaptation from a detected facial region and (ii) a self-adaptive scheme that creates a local model based on the response obtained using the global one. As a result of this hybrid adaptation, we obtain a local skin color model and we use it to extract seeds for the geodesic distance transform that determines the boundaries of skin regions. The results of our extensive experimental study confirm that the proposed algorithm outperforms several state-of-the-art methods, as well as our earlier adaptive skin detectors.
Keywords: Skin detection, skin segmentation, adaptive color model, spatial analysis
