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

Authors: Qing-Chuan Tao; Zhi-Ming Liu; George Bebis; Muhammad Hussain

Addresses: School of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China ' Department of Electrical and Biomedical Engineering, University of Nevada, Reno, Nevada, USA ' Department of Computer Science and Engineering, University of Nevada, Reno, Nevada, USA ' Department of Computer Science, King Saud University, Riyadh, Saudi Arabia

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).

Keywords: face recognition; colour images; local binary patterns; LBP; discrete wavelet transform; DWT; Fisher LDA; linear discriminant analysis; FLDA; non-parametric discriminant analysis; NDA; image representation; multi-scale local features; biometrics; principal component analysis; PCA.

DOI: 10.1504/IJBM.2015.071941

International Journal of Biometrics, 2015 Vol.7 No.3, pp.191 - 212

Received: 20 Oct 2014
Accepted: 01 May 2015

Published online: 24 Sep 2015 *

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