In this book, a new high performance face recognition system based
on matching the colour pixel statistics is introduced. A
pre-processing phase is introduced and applied to segment faces
from the background. Furthermore, a dedicated image equalization
method is introduced and implemented to minimize the illumination
problems of the images for further processing. The histogram of the
segmented face image as pixel statistics feature is used for face
recognition by cross correlating the histogram of a given face and
the histograms of faces in the database. Alternatively the
probability distribution functions of the images in different
colour channels, together with the Kullback-Leibler
Divergence/Distance (KLD) metric is also used for the recognition
of faces. Majority voting (MV) and feature vector fusion (FVF)
methods is briefly introduced and applied to combine feature
vectors obtained from different colour channels in HSI and YCbCr
colour spaces to improve recognition performance.
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