Comparison of gender recognition by PDP and radial basis function network

S.C. Yen, Paul Sajda, L. H. Finkel

Despite a long history of neurological, psychological, and computational
efforts, no satisfactory explanation has been offered for the extraordinary ability of
humans to recognize other human faces. However, a number of different network-
based approaches (Turk and Pentland,1991; Brunelli and Poggio, 1993; Buhmann et
al., 1989) have achieved surprisingly good ability to recognize faces, at least under
certain restricted conditions. We decided to compare the solutions developed by
different network architectures including PDP and radial basis function (RBF)
networks to the problem of gender classification. Given a picture of a face, including
external features such as hair, beard, jewelry, etc., the network must learn to
distinguish male from female. This is a simpler problem than general face
recognition, and there is some evidence that it is carried out by a separate population
of cells in the inferior temporal cortex (Damasio et. al., 1990).

Several investigators have previously applied PDP networks to the problem
of gender classification (Golomb et al., 1989; Cottrell and Metcalfe, 1989).
However, the hidden unit representations developed in those models were not analyzed
in detail. Moreover, we wanted to directly compare the representations developed by
different types of networks (PDP, RBF) when confronted with the exact same
training and test set

Accepted 1 January 1994
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