Abstract
In this chapter, we present the Morphable Model, a three-dimensional (3D) representation that enables the accurate modeling of any illumination and pose as well as the separation of these variations from the rest (identity and expression). The Morphable Model is a generative model consisting of a linear 3D shape and appearance model plus an imaging model, which maps the 3D surface onto an image. The 3D shape and appearance are modeled by taking linear combinations of a training set of example faces. We show that linear combinations yield a realistic face only if the set of example faces is in correspondence. A good generative model should accurately distinguish faces from non faces. This is encoded in the probability distribution over the model parameters, which assigns a high probability to faces and a low probability to non faces. The distribution is learned together with the shape and appearance space from the training data.
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Knothe, R., Amberg, B., Romdhani, S., Blanz, V., Vetter, T. (2011). Morphable Models of Faces. In: Li, S., Jain, A. (eds) Handbook of Face Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-932-1_6
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