Using the idea of the sparse representation to perform coarse-to-fine face recognition

Xu, Yong; Zhu, Qi; Fan, Zizhu; Zhang, David; Mi, Jian-Xun; Lai, Zhihui

doi:10.1016/j.ins.2013.02.051

Cited by 136 publications

(56 citation statements)

References 36 publications

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“…These images were captured over two sessions. The selected subset has also been widely used in previous studies [46,49,50]. Each image was down-sampled to …”

Section: Resultsmentioning

confidence: 99%

Dynamic dictionary optimization for sparse-representation-based face classification using local difference images

Shao

Song

Feng

et al. 2017

Information Sciences

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“…These images were captured over two sessions. The selected subset has also been widely used in previous studies [46,49,50]. Each image was down-sampled to …”

Section: Resultsmentioning

confidence: 99%

Dynamic dictionary optimization for sparse-representation-based face classification using local difference images

Shao

Song

Feng

et al. 2017

Information Sciences

View full text Add to dashboard Cite

“…The sparse representation classification (SRC) [19][20][21], recently proposed, can be regarded as a special form of least squares regression. Differing from LSR, it achieves an approximation of a test sample via a sparse linear combination of all training samples.…”

Section: Introductionmentioning

confidence: 99%

Kernel Negative ε Dragging Linear Regression for Pattern Classification

et al. 2017

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Linear regression (LR) and its variants have been widely used for classification problems. However, they usually predefine a strict binary label matrix which has no freedom to fit the samples. In addition, they cannot deal with complex real-world applications such as the case of face recognition where samples may not be linearly separable owing to varying poses, expressions, and illumination conditions. Therefore, in this paper, we propose the kernel negative dragging linear regression (KNDLR) method for robust classification on noised and nonlinear data. First, a technique called negative dragging is introduced for relaxing class labels and is integrated into the LR model for classification to properly treat the class margin of conventional linear regressions for obtaining robust result. Then, the data is implicitly mapped into a high dimensional kernel space by using the nonlinear mapping determined by a kernel function to make the data more linearly separable. Finally, our obtained KNDLR method is able to partially alleviate the problem of overfitting and can perform classification well for noised and deformable data. Experimental results show that the KNDLR classification algorithm obtains greater generalization performance and leads to better robust classification decision.

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“…In addition, based on the observation that the essential factor to achieve the highest accuracy of these RC methods is the collaborative representation rather than the sparse representation, various improved SRC methods have also proposed to further improve the classification accucary and efficiency. [20,21] Although these SRC methods achieve very good performance in many cases, they still have some challenges, such as limited training samples, various expressions and illuminations, etc. [22][23][24] To this end, various improved methods have been proposed, in which the most simple but effective methods are the virtual sample based methods.…”

Section: Introductionmentioning

confidence: 99%

The fusion of original and symmetric virtual images for image preprocessing in face recognition and collaborative representation based classification

Liu

2017

AIR

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Various poses, facial expressions and illuminations are the biggest challenges in the fields of face recognition. To overcome these challenges, we propose a simple yet novel method in this paper by using the approximately symmetrical virtual face. Firstly, based on the symmetrical characteristics of faces, we present the method to generate the virtual faces for all samples in detail. Secondly, the collaborative representation based classification method is performed on both of the original set and virtual set individually. In this way, two kinds of representation residuals of every class can be obtained. Thirdly, an adaptive weighted fusion approach is presented and utilized to integrate those two kinds of representation residuals for face recognition. Lastly, we can obtain the label of the test sample by assigning it to the class with the minimum fused residual. Several experiments are conducted which show that the proposed method not only can greatly improve the classification accuracy, but also can effectively reduce the negative influence of various poses, illuminations, and facial expressions.

show abstract

Using the idea of the sparse representation to perform coarse-to-fine face recognition

Cited by 136 publications

References 36 publications

Dynamic dictionary optimization for sparse-representation-based face classification using local difference images

Dynamic dictionary optimization for sparse-representation-based face classification using local difference images

Kernel Negative ε Dragging Linear Regression for Pattern Classification

The fusion of original and symmetric virtual images for image preprocessing in face recognition and collaborative representation based classification

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