Statistical models of face images — improving specificity

Statistical models of face images - improving specificity

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. In the paper we describe improved methods of modelling which couple shape and greylevel information more directly than our existi...

Adapted Statistical Models of Face Images

It has been previously demonstrated that systems based on local features and relatively complex statistical models, namely 1D Hidden Markov Models (HMMs) and pseudo-2D HMMs, are suitable for face recognition. Recently, a simpler statistical model, namely the Gaussian Mixture Model (GMM), was also shown to perform well. In much of the literature devoted to these models, the experiments were perf...

Statistical variation of Three Dimensional face models

Adapted Generative Models of Face Images

It has been previously demonstrated that systems based on local features and relatively complex generative models, namely 1D Hidden Markov Models (HMMs) and pseudo-2D HMMs, are suitable for face recognition. Recently, a simpler generative model, namely the Gaussian Mixture Model (GMM), was also shown to perform well. In most of the previous literature related to these models, the experiments we...

Higher-order Statistical Models of Visual Images

A probability model for visual images is an important component of many applications in image processing, computer vision, and computer graphics. The power of such a model comes from the fact that the set of visual images is enormous, and yet only a small fraction of these are likely to be encountered in a natural setting. Thus, a statistical prior model, even one that only partially captures t...