Volltext-Downloads (blau) und Frontdoor-Views (grau)
The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 9 of 1
Back to Result List

Evolutionary multi-objective optimization of neural networks for face detection

  • For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a feed-forward neural network (NN), e.g. in the Viisage-FaceFINDER® video surveillance system. We describe the optimisation of such a NN by a hybrid algorithm combining evolutionary multi-objective optimisation (EMO) and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy. We compare an EMO and a single objective approach, both with online search strategy adaptation. It turns out that EMO is preferable to the single objective approach in several respects.

Export metadata

Additional Services

Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Author:Stefan Wiegand, Christian Igel, Uwe Handmann
URL:http://www.worldscientific.com/doi/abs/10. 1142/S1469026804001288
DOI:https://doi.org/10.1142/S1469026804001288
Parent Title (English):International Journal of Computational Intelligence and Applications
Document Type:Article
Language:English
Year of Completion:2004
Release Date:2019/07/11
Volume:2004
Issue:4(3)
Page Number:16
First Page:237
Last Page:253
Institutes:Fachbereich 1 - Institut Informatik
DDC class:000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Licence (German):License LogoNo Creative Commons