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Genetic and evolutionary methods for biometric feature reduction

Genetic and evolutionary methods for biometric feature reduction In this paper, we investigate the use of Genetic and Evolutionary Computations (GECs) for feature selection, weighting, and hybrid weighting/selection in an attempt to increase recognitionaccuracy and reduce the number of features needed for biometric recognition. The GECs were first applied to a subset of subjects taken from the Facial Recognition Grand Challenge dataset. The resulting feature masks were then tested on a larger subset in an effort to investigate how well they generalise to unseen subjects. The results suggest that our GECs effectively increase the recognition accuracy, reduce the features needed, and create feature masks that generalise well. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2012.047642
Publisher site
See Article on Publisher Site

Abstract

In this paper, we investigate the use of Genetic and Evolutionary Computations (GECs) for feature selection, weighting, and hybrid weighting/selection in an attempt to increase recognitionaccuracy and reduce the number of features needed for biometric recognition. The GECs were first applied to a subset of subjects taken from the Facial Recognition Grand Challenge dataset. The resulting feature masks were then tested on a larger subset in an effort to investigate how well they generalise to unseen subjects. The results suggest that our GECs effectively increase the recognition accuracy, reduce the features needed, and create feature masks that generalise well.

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2012

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