Abstract
An approach to the detection and identification of human faces is
presented, and a working, near-real-time face recognition system which
tracks a subject's head and then recognizes the person by comparing
characteristics of the face to those of known individuals is described.
This approach treats 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 characteristic 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. The framework provides the ability to learn to
recognize new faces in an unsupervised manner
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