A Comparative Evaluation of Three Skin Color Detection Approaches

Abstract

Skin segmentation is a challenging task due to several influences such as, for example, unknown lighting conditions, skin colored background, and camera limitations. A lot of skin segmentation approaches were proposed in the past including adaptive (in the sense of updating the skin color online) and non-adaptive approaches. In this paper, we compare three different skin segmentation approaches. The first is a wellknown non-adaptive approach. It is based on a simple, pre-computed skin color distribution. Methods two and three adaptively estimate the skin color in each frame utilizing clustering algorithms. The second approach uses a hierarchical clustering for a simultaneous image and color space segmentation, while the third approach is a pure color space clustering, but with a more sophisticated clustering approach. For evaluation, we compared the segmentation results of the approaches against a ground truth dataset. To obtain the ground truth dataset, we labeled about 500 images captured under various conditions. Digital Peer Publishing Licence Any party may pass on this Work by electronic means and make it available for download under the terms and conditions of the current version of the Digital Peer Publishing Licence (DPPL). The text of the licence may be accessed and retrieved via Internet at http://www.dipp.nrw.de/. First presented at the Workshop Virtuelle und Erweiterte Realitt, 2012, extended and revised for JVRB

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Cite this paper

@inproceedings{Jensch2012ACE, title={A Comparative Evaluation of Three Skin Color Detection Approaches}, author={Dennis Jensch and Daniel Mohr and Gabriel Zachmann}, booktitle={JVRB}, year={2012} }