Abstract
This paper describes building models which represent the appearance of an object (in particular, a face) as seen from two or more di erent viewpoints simultaneously. A small number of 2D linear statistical models are suAEcient to capture the shape and appearance of a face from a wide range of viewpoints. Given multiple images of the same face we can learn a coupled model describing the relationship between the frontal appearance and the pro le of a face. This relationship can be used to predict new views of a face seen from one view. Such a coupled model can be used to constrain search algorithms which seek to locate a face in multiple views simultaneously, leading to more robust results than searching each view independently.
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