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Abstract

In human perception, as well as in machine vision, a crucial step in solving any object recognition task is an appropriate description of the object class under consideration. We emphasise this issue when considering the object class `human faces'. We discuss different representations that can be characterised by the degree of alignment between the images they provide for. The representations used span the whole range between a purely pixel-based image representation and a sophisticated model-based representation derived from the pixel-to-pixel correspondence between the faces [Vetter and Troje, 1995, in Mustererkennung Eds G Sagerer, S Posch, F Kummert (Berlin: Springer)]. The usefulness of these representations for sex classification was compared. This was done by first applying a Karhunen -- Loewe transformation on the representation to orthogonalise the data. A linear classifier was trained by means of a gradient-descent procedure. The classification error in a completely cross-validated simulation ranged from 15 in the simplest version of the pixel-based representation to 2.5 for the correspondence-based representation. However, even with intermediate representations very good performance was achieved.