Coarse to Fine Face Detection Based on Skin Color Adaption

In this paper we present a skin color approach for fast and accurate face detection which combines skin color learning and image segmentation. This approach starts from a coarse segmentation which provides regions of homogeneous statistical color distribution. Some regions represent parts of human skin and are selected by minimizing an error between the color distribution of each region and the output of a compression decompression neural network, which learns skin color distribution for several populations of different ethnicity. This ANN is used to find a collection of skin regions which are used to estimate the new parameters of the Gaussian models using a 2-means fuzzy clustering in order to adapt these parameters to the context of the input image. A Bayesian framework is used to perform a finer classification and makes the skin and face detection process invariant to scale and lighting conditions. Finally, a face shape based model is used to validate or not the face hypothesis on each skin region.