Abstract

This paper analyzes properties of a certain class of approximation techniques -- HyperBF networks -- in face perception tasks. The problem of gender classification and identification is addressed using a geometrical description of faces, extracted automatically from digitized pictures of frontal views of people without facial hair. The HyperBF networks perform satisfactorily on the classification tasks and exhibit the phenomenon of caricaturing, previously reported in psychophysical experiments. 1. Introduction Faces allow people to establish, among other things, the gender of a person, his (her) age, his (her) identity and, to a certain extent, emotions. In the current paper we address the tasks of gender classification and recognition. The work was done within MAIA, the integrated AI project under development at IRST, which aims to develop a face recognition system as one of its components ([1]). We will show that limited geometrical information may account for correct sex attributio...

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