Improved sparse representation with low-rank representation for robust face recognition

Zheng, Chun-Hou; Hou, Yi-Fu; Zhang, Jun

doi:10.1016/j.neucom.2015.07.146

Cited by 25 publications

(8 citation statements)

References 25 publications

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“…Dehghan et al (2015) used a portion of the training data for data modeling and used a knowledge score to define the result, which are features of confidence representation [17]. Zheng et al (2016) developing a comprehensive divided representation dictionary for facial recognition that can effectively overcome occlusion issues [18]. Gao et al (2017) offered a new strategy by generating a low range of the data representation and each fault image generated by the occlusion in real-time [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Face Recognition Use Local Image Dataset and Correlation Technique

Faiq

2021

UHD J SCI TECH

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Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.

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Section: Literature Reviewmentioning

confidence: 99%

Face Recognition Use Local Image Dataset and Correlation Technique

Faiq

2021

UHD J SCI TECH

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“…Consider the multi-view dimensionality reduction problem proposed in Section I. Previous studies have shown that LRR mentioned in Section II can spontaneously learn the low-rank representation coefficient matrix [38], [39]. And based on the coefficient matrix, the cross-view similarity matrix of sample points can be constructed, which does not need to select the global parameters as K-nearest neighbor algorithm [40] does, but can automatically obtain the adaptive neighborhood of sample points.…”

Section: A Lrmcca Modelmentioning

confidence: 99%

Multiset Canonical Correlations Analysis With Global Structure Preservation

Zhang

Jing

2020

IEEE Access

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This paper considers unsupervised dimensionality reduction of multi-view data, where locality preserving canonical correlation analysis and a new locality-preserving canonical correlation analysis are two typical effective methods. However, they ignore the global structure while considering the local structure of data, and are sensitive to noises because of the relationship of neighbors based on the Euclidean distance. In this paper, we propose a novel multi-view dimensionality reduction method: multiset canonical correlations analysis based on low-rank representation. Our model introduces the cross-view similarity matrix to consider the correlation of all different points in cross views, which makes it not only preserve the local structure but also the global structure of data. And the cross-view similarity matrix is constructed by using low-rank representation, which can make the model more robust. In addition, a parameter β is introduced to adjust the importance of the correlation of different sample points, enhancing the generalization ability for different datasets. Experiments on four multi-view datasets show our proposed method has better performance than the related methods.

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“…Unlike these sparse representation methods which use the 1-D pixel-based error model to address the face classification problem, the works [29,37,40,46,47,19] introduced the local consistency concept (nearby data points share the same properties) by imposing that the error images have low-rank or approximately low-rank structure. As a convex relaxation of the rank-function, in [40] the authors proposed the minimization of the nuclear norm of the error matrix.…”

Section: Related Workmentioning

confidence: 99%

A Robust Group-Sparse Representation Variational Method With Applications to Face Recognition

Keinert

Lazzaro

Morigi

2019

IEEE Trans. on Image Process.

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In this paper we propose a Group-Sparse Representation based method with applications to Face Recognition (GSR-FR). The novel sparse representation variational model includes a nonconvex sparsity-inducing penalty and a robust non-convex loss function. The penalty encourages group sparsity by using approximation of the 0 -quasinorm, and the loss function is chosen to make the algorithm robust to noise, occlusions and disguises. The solution of the non-trivial non-convex optimization problem is efficiently obtained by a majorization-minimization strategy combined with forward-backward splitting, which in particular reduces the solution to a sequence of easier convex optimization sub-problems. Extensive experiments on widely used face databases show the potentiality of the proposed model and demonstrate that the GSR-FR algorithm is competitive with state-of-the-art methods based on sparse representation, especially for very low dimensional feature spaces.

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Improved sparse representation with low-rank representation for robust face recognition

Cited by 25 publications

References 25 publications

Face Recognition Use Local Image Dataset and Correlation Technique

Face Recognition Use Local Image Dataset and Correlation Technique

Multiset Canonical Correlations Analysis With Global Structure Preservation

A Robust Group-Sparse Representation Variational Method With Applications to Face Recognition

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