Sparse representation for face recognition based on discriminative low-rank dictionary learning

2012 ◽  
Author(s):  
Long Ma ◽  
Chunheng Wang ◽  
Baihua Xiao ◽  
Wen Zhou
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Dictionary Learning ◽  
Low Rank

Virtual samples based robust block-diagonal dictionary learning for face recognition

2021 ◽  
Vol 25 (5) ◽  
pp. 1273-1290
Author(s):  
Shuangxi Wang ◽  
Hongwei Ge ◽  
Jinlong Yang ◽  
Shuzhi Su
Keyword(s):  
Face Recognition ◽  
Dictionary Learning ◽  
Recognition Performance ◽  
Small Sample Size ◽  
Small Sample ◽  
Low Rank ◽  
Negative Effects ◽  
Linear Classifier ◽  
Virtual Samples ◽  
Block Diagonal

It is an open question to learn an over-complete dictionary from a limited number of face samples, and the inherent attributes of the samples are underutilized. Besides, the recognition performance may be adversely affected by the noise (and outliers), and the strict binary label based linear classifier is not appropriate for face recognition. To solve above problems, we propose a virtual samples based robust block-diagonal dictionary learning for face recognition. In the proposed model, the original samples and virtual samples are combined to solve the small sample size problem, and both the structure constraint and the low rank constraint are exploited to preserve the intrinsic attributes of the samples. In addition, the fidelity term can effectively reduce negative effects of noise (and outliers), and the ε-dragging is utilized to promote the performance of the linear classifier. Finally, extensive experiments are conducted in comparison with many state-of-the-art methods on benchmark face datasets, and experimental results demonstrate the efficacy of the proposed method.

Multi-spectral low-rank structured dictionary learning for face recognition

2016 ◽  
Vol 59 ◽  
pp. 14-25 ◽  
Author(s):  
Xiao-Yuan Jing ◽  
Fei Wu ◽  
Xiaoke Zhu ◽  
Xiwei Dong ◽  
Fei Ma ◽  
...  
Keyword(s):  
Face Recognition ◽  
Dictionary Learning ◽  
Low Rank ◽  
Structured Dictionary

scholarly journals Robust face recognition via low-rank sparse representation-based classification

2015 ◽  
Vol 12 (6) ◽  
pp. 579-587 ◽  
Author(s):  
Hai-Shun Du ◽  
Qing-Pu Hu ◽  
Dian-Feng Qiao ◽  
Ioannis Pitas
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Low Rank ◽  
Robust Face Recognition

scholarly journals Ensemble Dictionary Learning for Single Image Deblurring via Low-Rank Regularization

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1143 ◽  
Author(s):  
Jinyang Li ◽  
Zhijing Liu
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Clustering Algorithms ◽  
Solution Space ◽  
Image Deblurring ◽  
Statistical Technique ◽  
Low Rank ◽  
Single Image ◽  
Norm Minimization ◽  
Visual Artifacts

Sparse representation is a powerful statistical technique that has been widely utilized in image restoration applications. In this paper, an improved sparse representation model regularized by a low-rank constraint is proposed for single image deblurring. The key motivation for the proposed model lies in the observation that natural images are full of self-repetitive structures and they can be represented by similar patterns. However, as input images contain noise, blur, and other visual artifacts, extracting nonlocal similarities only with patch clustering algorithms is insufficient. In this paper, we first propose an ensemble dictionary learning method to represent different similar patterns. Then, low-rank embedded regularization is directly imposed on inputs to regularize the desired solution space which favors natural and sharp structures. The proposed method can be optimized by alternatively solving nuclear norm minimization and l 1 norm minimization problems to achieve higher restoration quality. Experimental comparisons validate the superior results of the proposed method compared with other deblurring algorithms in terms of visual quality and quantitative metrics.

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