Hybrid Ensembles Using Hopfield Neural Networks and Haar-Like Features for Face Detection

The success of an ensemble of classifiers depends on the diversity of the underlying features. If a classifier can address more different aspects of the analyzed objects, this allows to improve an ensemble. In this paper we propose an ensemble using as classifier members a Hopfield Neural Network (HNN) that uses Haar-like features as an input template. We analyse the HNN as the only classifier type and also combine it with threshold classifiers to a hybrid neural ensemble, so that the resulting ensemble contains –as members– threshold and neural classifiers. This ensemble architecture is evaluated for the domain of face detection. We show that a HNN that uses summed pixel intensities as input for the classification has the ability to improve the performance of an ensemble.