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Fast SRC using quadratic optimisation in downsized coefficient solution subspace
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DOI：10.1016/j.sigpro.2019.03.007.png)
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Abstract
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Extended sparse representation-based classification (ESRC) has shown interesting results on the problem of under-sampled 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 l i -minimization problem. To address this issue, this paper proposes two strategies to speed up SRC using quadratic optimisation in downsized coefficient solution subspace. The first 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 classification, using the downsized coefficient space. Secondly, to explore the positive impact of our proposed method on deep-learningbased face classification, 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. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Fast SRC
Dimensionality reduction
CNN-Based features
Face recognition
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