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Fast SRC using quadratic optimisation in downsized coefficient solution subspace

Song, Xiaoning, Hu, Guosheng, Luo, Jian-Hao, Feng, Zhenhua, Yu, Dong-Jun and Wu, Xiao-Jun (2019) Fast SRC using quadratic optimisation in downsized coefficient solution subspace Signal Processing, 161. pp. 101-110.

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Extended sparse representation-based classifcation (ESRC) has shown interesting results on the problem of undersampled face recognition by generating an auxiliary intraclass variant dictionary for the representation of possible appearance variations. However, the method has high computational complexity due to the l1-minimization problem. To address this issue, this paper proposes two strategies to speed up SRC using quadratic optimisation in downsized coefient solution subspace. The frst one, namely Fast SRC using Quadratic Optimisation (FSRC-QO), applies PCA and LDA hybrid constrained optimisation method to achieve compressed linear representations of test samples. By design, more accurate and discriminative reconstruction of a test sample can be achieved for face classifcation, using the downsized coefficient space. Secondly, to explore the positive impact of our proposed method on deep-learning-based face classifcation, we enhance FSRC-QO using CNN-based features (FSRC-QO-CNN), in which we replace the original input image using robust CNN features in our FSRC-QO framework. Experimental results conducted on a set of well known face datasets, including AR, FERET, LFW and FRGC, demonstrate the merits of the proposed methods, especially in computational efficiency.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
Song, Xiaoning
Hu, Guosheng
Luo, Jian-Hao
Yu, Dong-Jun
Wu, Xiao-Jun
Date : 10 March 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1016/j.sigpro.2019.03.007
Grant Title : FACER2VM
Copyright Disclaimer : © 2019 Elsevier B.V. All rights reserved.
Depositing User : Rebecca Cooper
Date Deposited : 10 Apr 2019 09:44
Last Modified : 11 Mar 2020 02:08

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