Abstract
We describe the use of flexible models for representing the shape and grey-level appearance of human faces. These models are controlled by a small number of parameters which can be used to code the overall appearance of a face for image compression and classification purposes. The model parameters control both inter-class and within-class variation. Discriminant analysis techniques are employed to enhance the effect of those parameters affecting inter-class variation, which are useful for classification. We have performed experiments using face images which display considerable variability in 3D viewpoint, lighting and facial expression. We show that good face reconstructions can be obtained using 83 model parameters, and that high recognition rates can be achieved.
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