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Discriminative Sparsity Graph Embedding for Unconstrained Face Recognition

1,2,*, 1,* and 1
1
Department of Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China
2
College of Communication Engineering, The Army Engineering University of PLA, Nanjing 210007, China
*
Authors to whom correspondence should be addressed.
Electronics 2019, 8(5), 503; https://doi.org/10.3390/electronics8050503
Received: 30 March 2019 / Revised: 25 April 2019 / Accepted: 1 May 2019 / Published: 7 May 2019
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Abstract

In this paper, we propose a new dimensionality reduction method named Discriminative Sparsity Graph Embedding (DSGE) which considers the local structure information and the global distribution information simultaneously. Firstly, we adopt the intra-class compactness constraint to automatically construct the intrinsic adjacent graph, which enhances the reconstruction relationship between the given sample and the non-neighbor samples with the same class. Meanwhile, the inter-class compactness constraint is exploited to construct the penalty adjacent graph, which reduces the reconstruction influence between the given sample and the pseudo-neighbor samples with the different classes. Then, the global distribution constraints are introduced to the projection objective function for seeking the optimal subspace which compacts intra-classes samples and alienates inter-classes samples at the same time. Extensive experiments are carried out on AR, Extended Yale B, LFW and PubFig databases which are four representative face datasets, and the corresponding experimental results illustrate the effectiveness of our proposed method. View Full-Text
Keywords: unconstrained face recognition; dimensionality reduction; manifold learning; sparse preserving projections unconstrained face recognition; dimensionality reduction; manifold learning; sparse preserving projections
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Tong, Y.; Zhang, J.; Chen, R. Discriminative Sparsity Graph Embedding for Unconstrained Face Recognition. Electronics 2019, 8, 503.

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