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)
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)
Most research in recognising human faces consists of full frontal view images and operate under strict imaging conditions such as controlled illumination and limited facial expressions. Face recognition using multiple views in the viewing sphere is a more diicult task since face rotations out of the imaging plane can introduce occlusion of facial(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)
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