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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.

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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)
Pagenumber: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