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Face recognition using a novel image representation scheme and multi-scale local features

Face recognition using a novel image representation scheme and multi-scale local features This paper presents a new method for improving face recognition performance under difficult conditions. Specifically, a new image representation scheme is proposed which is derived from the YCrQ colour space using principal component analysis (PCA) followed by Fisher linear discriminant analysis (FLDA). A multi-scale local feature, LBP-DWT, is used for face representation which is computed by extracting different resolution local binary patterns (LBP) features from the new image representation and transforming the LBP features into the wavelet domain using discrete wavelet transform (DWT) and Haar wavelets. A variant of non-parametric discriminant analysis (NDA), called regularised non-parametric discriminant analysis (RNDA) is introduced to extract the most discriminating features from LBP-DWT. The proposed methodology has been evaluated using two challenging face databases (FERET and multi-PIE). The promising experimental results show that the proposed method outperforms two state-of-the-art methods, one based on Gabor features and the other based on sparse representation classification (SRC). Copyright © 2015 Inderscience Enterprises Ltd. Q-C. Tao et al. Keywords: face recognition; colour image; local binary patterns; LBP; discrete wavelet transform; DWT; Fisher linear discriminant analysis; FLDA; non-parametric discriminant analysis; NDA. Reference to this paper should be made as follows: Tao, Q-C., Liu, Z-M., Bebis, G. and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Face recognition using a novel image representation scheme and multi-scale local features

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Publisher
Inderscience Publishers
Copyright
Copyright © 2015 Inderscience Enterprises Ltd.
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2015.071941
Publisher site
See Article on Publisher Site

Abstract

This paper presents a new method for improving face recognition performance under difficult conditions. Specifically, a new image representation scheme is proposed which is derived from the YCrQ colour space using principal component analysis (PCA) followed by Fisher linear discriminant analysis (FLDA). A multi-scale local feature, LBP-DWT, is used for face representation which is computed by extracting different resolution local binary patterns (LBP) features from the new image representation and transforming the LBP features into the wavelet domain using discrete wavelet transform (DWT) and Haar wavelets. A variant of non-parametric discriminant analysis (NDA), called regularised non-parametric discriminant analysis (RNDA) is introduced to extract the most discriminating features from LBP-DWT. The proposed methodology has been evaluated using two challenging face databases (FERET and multi-PIE). The promising experimental results show that the proposed method outperforms two state-of-the-art methods, one based on Gabor features and the other based on sparse representation classification (SRC). Copyright © 2015 Inderscience Enterprises Ltd. Q-C. Tao et al. Keywords: face recognition; colour image; local binary patterns; LBP; discrete wavelet transform; DWT; Fisher linear discriminant analysis; FLDA; non-parametric discriminant analysis; NDA. Reference to this paper should be made as follows: Tao, Q-C., Liu, Z-M., Bebis, G. and

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2015

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