Using MLP and RBF neural networks for face recognition: An insightful comparative case study

In this paper, two architectures neural network (NN) classifier models have been compared, multilayer perceptron (MLP) neural network with back-propagation algorithm and radial basis function (RBF) neural network. Capabilities of the presented architectures have been compared. The feature projection vectors, obtained through the Principal Component Analysis or called Eigenfaces method, are used as the input vectors for the training and testing of both NN architectures. Several factors affect the recognition performance; experimental results are applied to the ORL database which contains variability in expression, pose, and facial details. The experimental result showed that the Eigenfaces/RBF system has recognition error rates that are lower than those of the Eigenfaces/MLP system by 3%. Thus the Eigenfaces/RBF system performs better than the Eigenfaces/MLP system in terms of correct recognition rates and training convergence speed of the network.