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Modelling the variability in face images

Model based approaches to the interpretation of face images have proved very successful. We have previously described statistically based models of face shape and grey-level appearance and shown how they can be used to perform various coding and interpretation tasks (Lanitis et al., 1995). In the paper we describe improved methods of modelling, which couple shape and grey-level information more directly than our existing methods, isolate the changes in appearance due to different sources of variability (person, expression, pose, lighting) and deal with nonlinear shape variation. We show that the new methods are better suited to interpretation and tracking tasks.