Abstract
We describe a novel approach for fully automated face recognition and show its feasibility on a large database of facial images (FERET). Our approach, based on a hybrid architecture consisting of an ensemble of radial basis function (RBF) neural networks and inductive decision trees, combines the merits of "abstractive" features with those of "holistic" template matching. The benefits of our architecture include: 1) robust detection of facial landmarks using decision trees, and 2) robust face recognition using consensus methods over ensembles of RBF networks. Experiments carried out using k-fold cross validation on a large database consisting of 748 images corresponding to 374 subjects, among them 11 duplicates, yield on the average 87% correct match, and 99% correct surveillance ("verification").
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