Modelling profiles with a mixture of Gaussians
Point distribution models are useful tools for modelling the variability of particular classes of shapes. A common approach is to apply a principle component analysis to the data, to reduce the dimensionality of the representation. However, a single multivariate Gaussian model of the probability density, estimated from the principle covariances, can be substantially inaccurate. We examine how the specificity of a model can be improved by using a mixture of Gaussians, trained with the expectation-maximization algorithm, with reference to hand and vehicle profiles.