Abstract

Increasingly the problems of recognising faces under
a variety of viewing conditions, including depth
rotations, is being considered in the field. The concept
of norm-based coding an face recognition is not
new but has been little investigated in machine models.
Here we describe a norm-based face recognition system
which is capable of generalising from a single training
view to recognise novel views of target faces. The system
is based upon the characteristic nature of faces as
they move through a pose-varying eigenspace of facial
images and deviations from the norm of a gallery of
face images. We illustrate the use of the technique for
a large range of pose variation.