Hybrid and parallel face classifier based on artificial neural networks and principal component analysis

Presents a hybrid and parallel system based on artificial neural networks for a face invariant classifier and general pattern recognition problems. A set of face features is extracted by using the eigenpaxel method, which is based on principal component analysis (PCA) of a group of pixels, that is called a paxel. To classify subjects, multi-layer perceptron neural networks (NNs) are trained for each eigenpaxel. These parallel NN kernels provide sage, fast and efficient classification. To combine the results of parallel NNs, a novel judge analyzer is proposed based on bond rating classification and prediction. The proposed judge strategy can detect distinguishable face features even in arguable situations. The proposed method was evaluated on Olivetti and HongIk university (HIU) face databases and it yields a top recognition rate of 95.5% and 94.11% respectively, which are better results than the previous eigenpaxel and NN approach.