Introduction - If you have any usage issues, please Google them yourself
The way this algorhitm works is by treating face recognition as a
* two-dimensional recognition problem, taking advantage of the fact that
* faces are normally upright and thus may be described by a small set
* of 2-D characterisits views. Face images are projected onto a
* feature space ( face space ) that best encodes the variation
* among known face images. The face space is defined by the
* eigenfaces , which are the eigenvectors of the set of faces
* they do not necessarily correspond to isolated features such as eyes,
* ears, and noses. (quoted the paper s abstract)