Hybrid ensembles using hopfield neural networks and haar-like features for face detection

Authors:
Nils Meins;Stefan Wermter;Cornelius Weber
Affiliations:
Department of Informatics, Knowledge Technology, University of Hamburg, Hamburg, Germany;Department of Informatics, Knowledge Technology, University of Hamburg, Hamburg, Germany;Department of Informatics, Knowledge Technology, University of Hamburg, Hamburg, Germany
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

Citing 6
Cited 0

The Strength of Weak Learnability

Machine Learning
Boosting a weak learning algorithm by majority

Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A General Framework for Object Detection

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Joint Haar-like Features for Face Detection

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Neural network face identification

AIASABEBI'11 Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.