Different eigenspace-based approaches have been proposed for the recognition of faces. They differ mostly in the kind of projection method been used, in the projection algorithm been employed, in the use of simple or differential images before/after projection, and in the similarity matching criterion or classification method employed. Statistical, neural, fuzzy and evolutionary algorithms are used in the implementation of those systems. The aim of this paper is to present an independent, comparative study between some of these hybrid eigenspace-based approaches. This study considers theoretical aspects as well as simulations performed using a small face database (Yale Face Database) and a large face database (FERET).
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