Combined Supervised / Unsupervised Algorithm for Skin Detection: A Preliminary Phase for Face Detection
Skin detection in color images is of great importance for the computer vision research community. Many existing skin detection algorithms are characterized by high false detection rates. The proposed algorithm performs offline learning of skin color, but also of "false-skin colors", which may be misclassified as skin using regular histogram or Gaussian skin color models. This supervised learning of false-skin colors produces a significant reduction in false detection rates. Our aim is to extract skin blobs that are suspected to contain faces, which are usually ellipsoid-shaped.
Thus, to extract these blobs, an unsupervised optimal fuzzy clustering (UOFC) algorithm is applied in the spatial image space. Blobs segmented by the clustering procedure are then examined by specific features, e.g. geometrical, to classify them as face candidates.
Sample runs of the algorithm on a bank of images show high skin detection rates with reduced false detection rates.
KeywordsSkin Detection Supervised Learning Unsupervised Learning Optimal Fuzzy Clustering Likelihood Estimation
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