Principal component and neural network analyses of face images: What can be generalized in gender classification?

We present an overview of the major findings of the principal component analysis (PCA) approach to facial analysis. In a neural network or connectionist framework, this approach is known as the linear autoassociator approach. Faces are represented as a weighted sum of macrofeatures (eigenvectors or eigenfaces) extracted from a cross-product matrix of face images. Using gender categorization as an illustration, we analyze the robustness of this type of facial representation. We show that eigenvectors representing general categorical information can be estimated using a very small set of faces and that the information they convey is generalizable to new faces of the same population and to a lesser extent to new faces of a different population. (C) 1997 Academic Press.