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A merging stage is then iteratively performed on the set
of homogeneous skin color regions in the color quantized image,
in order to provide a set of potential face areas. Constraints
related to shape and size of faces are applied, and face intensity
texture is analyzed by performing a wavelet packet decomposition
on each face area candidate in order to detect human faces. The
wavelet coefficients of the band filtered images characterize the
face texture and a set of simple statistical deviations is extracted
in order to form compact and meaningful feature vectors. Then,
an efficient and reliable probabilistic metric derived from the
Bhattacharrya distance is used in order to classify the extracted
feature vectors into face or nonface areas, using some prototype
face area vectors, acquired in a previous training stage...
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