  This research starts with the observation that face recognition can suffer a
low impact from significant image shrinkage. To explain this fact, we proposed
the Pixel Clustering methodology. It defines regions in the image in which its
pixels are very similar to each other. We extract features from each region. We
used three face databases in the experiments. We noticed that 512 is the
maximum number of features needed for high accuracy image recognition. The
proposed method is also robust, even if only it uses a few classes from the
training set.
