EMPATH: Face, Emotion, and Gender Recognition Using Holons

The network is trained to simply reproduce its input, and so can as a nonlinear version of Kohonen's (1977) auto-associator. However it must through a narrow channel of hidden units, so it must extract regularities from the during learning. Empirical analysis of the trained network showed that the span the principal subspace of the image vectors, with some noise on the component due to network nonlinearity (Cottrell & Munro, 1988).