Ensemble and Modular Approaches for Face Detection: A Comparison
Abstract
A new learning model based on autoassociative neural networks is developped and applied to face detection. To extend the de(cid:173) tection ability in orientation and to decrease the number of false alarms, different combinations of networks are tested: ensemble, conditional ensemble and conditional mixture of networks. The use of a conditional mixture of networks allows to obtain state of the art results on different benchmark face databases.
Cite
Text
Feraud and Bernier. "Ensemble and Modular Approaches for Face Detection: A Comparison." Neural Information Processing Systems, 1997.Markdown
[Feraud and Bernier. "Ensemble and Modular Approaches for Face Detection: A Comparison." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/feraud1997neurips-ensemble/)BibTeX
@inproceedings{feraud1997neurips-ensemble,
title = {{Ensemble and Modular Approaches for Face Detection: A Comparison}},
author = {Feraud, Raphaël and Bernier, Olivier},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {472-478},
url = {https://mlanthology.org/neurips/1997/feraud1997neurips-ensemble/}
}