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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.