Abstract: A statistical shape model is built by automatically establishing correspondence between a set of two dimensional shapes or three dimensional shapes. A parameterization of each shape is determined and, a statistical shape model is built using the parameterization. An objective function is used to provide an output which indicates the quality of the statistical shape model. By performing these steps repeatedly for different parameterizations and comparing the quality of the resulting statistical shape models using output of the objective function to determine which parameterization provides the statistical shape model having the best quality, the output of the objective function is a measure of the quantity of information required to code the set of shapes using the statistical shape model.

Abstract: A method of object identification comprising, for an image in which an object is to be identified, determining for each of a set of location in the direction in which the gradient of intensity change to an adjacent location is greatest together with the magnitude of that gradient, modifying the gradient magnitude using a nonlinear function, the modified gradient and the associated direction for the set of location providing a vector representative of the image, and comparing the vector with a previously generated statistical model which provides identification of the object.

Abstract: A method of determining face sub-spaces includes making initial estimates of the sub-spaces, for example lighting, pose, identity and expression, using Principle Component Analysis on appropriate groups of faces. An iterative algorithm is applied to image codings to maximize the probability of coding across these non-orthogonal sub-spaces, obtaining the projection on each sub-space, and recalculating the spaces.

Abstract: A method of object identification comprising, for an image in which an object is to be identified, determining for each of a set of locations in the image the direction in which the gradient of intensity change to an adjacent location is greatest together with the magnitude of that gradient, modifying the gradient magnitude using a nonlinear function, the modified gradient and the associated direction for the set of location providing a vector representative of the image, and comparing the vector with a previously generated statistical model which provides identification of the object.