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

We present 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 pixel, that is called a paxel. To classify subjects, multi-layer perceptron neural network (NN)s 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 that a top recognition rates are 95.5% and 94.11% respectively, which are better results than the previous eigenpaxel and NN approach [1].