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.

@InProceedings{MWW12, 
 	 author =  {Meins, Nils and Wermter, Stefan and Weber, Cornelius},  
 	 title = {Hybrid Ensembles Using Hopfield Neural Networks and Haar-like Features for Face Detection}, 
 	 booktitle = {Proceedings of the 22nd International Conference on Artificial Neural Networks (ICANN 2012)},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {403--410},
 	 year = {2012},
 	 month = {Sep},
 	 publisher = {Springer Heidelberg},
 	 doi = {}, 
 }