A procedure for fast training and evaluation image classification systems
using support vector machines (SVMs) with linear input features of high
dimensionality is presented. The linear input features are derived from
raw image data by means of a set of m linear functions defined on the
k-dimensional raw input data, and are used for image classification,
including facial recognition tasks.