Adaptive momentum Levenberg-Marquardt RBF for face recognition

This paper investigates the application of Levenberg-Marquardt (LM) based radial basis function (RBF) neural networks for face recognition. The contribution of this paper is two-fold. First, we propose the use of Levenberg-Marquardt (LM) and adaptive momentum LM algorithm to update the weights and network parameters (centers and width). The purpose of the proposal of the latter algorithm is to further increase the learning efficiency of the RBF neural network. The second contribution of the paper is the adaptation of the high computational complexity LM-based RBF neural networks to the complex problem of face recognition. To reduce the computations required, dimension reduction was applied prior to the training of the networks. In addition to that, we have also proposed the use of prior knowledge to guess the initial values of the weights during initialization as oppose to random weights. The proposed methods were tested on the Yale database and were found to yield positive results that can further improve the learning efficiency of the networks for the application of face recognition.