Stefan Aeberhard

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ÐMuch research in human face recognition involves fronto-parallel face images, constrained rotations in and out of the plane, and operates under strict imaging conditions such as controlled illumination and limited facial expressions. Face recognition using multiple views in the viewing sphere is a more difficult task since face rotations out of the imaging(More)
View-based recognition is a simple, relatively robust method for object recognition. Current techniques use small, simplistic object databases requiring, in many cases, large processing training and/or recognition times. In this paper we propose an extension to the view-based object recognition paradigm using lines or transects of 2D image views together(More)
We report on an extensive simulation study comparing eight statistical classiication methods, focusing on problems where the number of observations is less than the number of variables. Using a wide range of artiicial and real data, two types of classiiers were contrasted; methods that classify using all variables, and methods that rst reduce the number of(More)
Variable selection is an important methodology in multivariate statistics, especially in the context of classiication. However, because the direct evaluation of the subsets using a classiier has been computationally too expensive in the past for a medium to large number of variables, variable selection has instead been performed using simple measures of(More)
Regularised Discriminant Analysis has proven to be a most eeective classiier for problems where traditional classiiers fail because of a lack of suucient training samples, as is often the case in high dimensional settings. However, it has been shown that the model selection procedure of Regularised Discriminant Analysis, determining the degree of(More)