A survey of skin-color modeling and detection methods
Pattern Recognition
An adaptive multiple model approach for fast content-based skin detection in on-line videos
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
A combined skin model and feature approach for tracking of human faces
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Universal seed skin segmentation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Color based skin classification
Pattern Recognition Letters
A skin detection approach based on the Dempster--Shafer theory of evidence
International Journal of Approximate Reasoning
A new approach to signature-based authentication
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Systematic skin segmentation: merging spatial and non-spatial data
Multimedia Tools and Applications
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Mixture models are frequently used to fit skin color distributions in various color spaces. However, the high computational cost of the conventional EM algorithm makes it intractable for large data sets. In this paper, we propose a novel algorithm for estimating the parameters of mixture models. Multidimensional histograms are incorporated into the EM framework to group neighboring datapoints and reduce the size of the data set. We adopt this method to build Gaussian mixture models of skin color and compare the performance of models with different number of components. Further experiments on synthetic data show the efficiency of our method as a general approach to data clustering.