Adaboost and Hopfield Neural Networks on Different Image Representations for Robust Face Detection
Proceedings of the 12th International Conference on Hybrid Intelligent Systems (HIS 2012),
pages 531--536,
doi: 10.1109/HIS.2012.6421390
- Dec 2012
Associated documents :
Face detection is an active research area comprising the fields of computer vision, machine learning and
intelligent robotics. However, this area is still challenging due
to many problems arising from image processing and the
further steps necessary for the detection process. In this work
we focus on Hopfield Neural Network (HNN) and ensemble
learning. It extends our recent work by two components:
the simultaneous usage of different image representations and
combinations as well as variations in the training procedure.
Using the HNN within an ensemble achieves high detection
rates but shows no increase in false detection rates, as is
commonly the case. We present our experimental setup and
investigate the robustness of our architecture. Our results
indicate, that with the presented methods the face detection
system is flexible regarding varying environmental conditions,
leading to a higher robustness.
@InProceedings{MJWW12,
author = {Meins, Nils and Jirak, Doreen and Weber, Cornelius and Wermter, Stefan},
title = {Adaboost and Hopfield Neural Networks on Different Image Representations for Robust Face Detection},
booktitle = {Proceedings of the 12th International Conference on Hybrid Intelligent Systems (HIS 2012)},
editors = {},
number = {},
volume = {},
pages = {531--536},
year = {2012},
month = {Dec},
publisher = {IEEE},
doi = {10.1109/HIS.2012.6421390},
}