Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition

A face recognition (FR) problem involves the face detection, representation and classi cation steps. Once a face is located in an image, it has to be represented through a feature extraction process, for later performing a proper face classification task. The most widely used approach for feature extraction is the eigenfaces method, where an eigenspace is established from the image training samples using principal components analysis. In the classification phase, an input face is projected to the obtained eigenspace and classifed by an appropriate classifer. Neural network classifers based on multilayer perceptron models have proven to be well suited to this task. This paper presents an array of multilayer perceptron neural networks trained with a novel no-class resampling strategy which takes into account the balance problem between class and no-class examples and increases the generalization capabilities. The proposed model is compared against a classical multilayer perceptron classifer for face recognition over the AT&T database of faces, obtaining results that show an improvement over the classification rates of a classical classifer.