Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6569
Title: Evolutionary discriminant feature extraction with application to face recognition
Authors: Zhao, Q
Zhang, DD 
Zhang, L 
Lu, H
Keywords: Computation theory
Computational efficiency
Feature extraction
Issue Date: 3-Sep-2009
Publisher: Springer
Source: EURASIP Journal on advances in signal processing, 2 Sept. 2009, v. 2009, 465193, p. 1-12 How to cite?
Journal: EURASIP Journal on advances in signal processing 
Abstract: Evolutionary computation algorithms have recently been explored to extract features and applied to face recognition. However these methods have high space complexity and thus are not efficient or even impossible to be directly applied to real world applications such as face recognition where the data have very high dimensionality or very large scale. In this paper, we propose a new evolutionary approach to extracting discriminant features with low space complexity and high search efficiency. The proposed approach is further improved by using the bagging technique. Compared with the conventional subspace analysis methods such as PCA and LDA, the proposed methods can automatically select the dimensionality of feature space from the classification viewpoint. We have evaluated the proposed methods in comparison with some state-of-the-art methods using the ORL and AR face databases. The experimental results demonstrated that the proposed approach can successfully reduce the space complexity and enhance the recognition performance. In addition, the proposed approach provides an effective way to investigate the discriminative power of different feature subspaces.
URI: http://hdl.handle.net/10397/6569
ISSN: 1687-6180
DOI: 10.1155/2009/465193
Rights: Copyright © 2009 Qijun Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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