A supervised dimensionality reduction method-based sparse representation for face recognition

Zhang, Xinxin; Peng, Yali; Liu, Shigang; Wu, Jie; Ren, Pingan

doi:10.1080/09500340.2016.1260781

Cited by 10 publications

(7 citation statements)

References 27 publications

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“…In the meanwhile, SRC, CRC, LRC, coarse to fine K nearest neighbor classification (CFKNNC) [56], improvement to nearest neighbor classification (INNC), homotopy [7], primal augmented lagrangian method (PLAM) [60], the method [65], discriminative sparse representation method (DSRM) [59], block-diagonal representation (BDLRR) [66] and the method [27] were used to compare with our method. Moreover, we set the parameter μ of our method as 0.01.…”

Section: Resultsmentioning

confidence: 99%

“…A lot of face recognition methods have been applied to identity authentication and security system [3,4,11,21,23]. However, face recognition still faces a lot of challenges such as the various lightings, facial expressions, poses and environments [10,13,14,33,35,42,54,57,65,68]. In order to overcome these challenges, a lot of representation-based classification methods (RBCMs) [15,29,31,37,52,53,63,64,68] are proposed such as SRC [52], collaborative representation classification (CRC) [63], two-phase test sample representation (TPTSR) [53], linear regression classification (LRC) [37], feature space representation method [61], an improvement to the nearest neighbor (INNC) classification [55], etc.…”

Section: Introductionmentioning

confidence: 99%

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A novel grouped sparse representation for face recognition

Peng

Liu

et al. 2018

Multimed Tools Appl

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Grouped sparse representation classification methods (GSRCMs) have been attracted much attention by scholars, especially in face recognition. However, pervious literatures of GSRCMs only fuse the scores from different groups to classification the test sample, not consider relationships of the groups. Moreover, in real-world application, many methods of face recognition cannot obtain satisfied recognition accuracies because of the variation of poses, illuminations and facial representations of face image. In order to overcome above-mentioned bottlenecks, in this paper, we proposed a novel grouped fusion-based method in face recognition. The proposed method uses the axis-symmetrical property of face to designs a framework and perform it on original training set to generate a kind of virtual samples. The virtual samples are able to reflect the possible change of face images. Meanwhile, to consider the relationship of different groups and strengthen the representation capability of test sample, the proposed method exploits a novel weighted fusion approach to classify the test sample. Experimental results on five face databases demonstrate that our

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel grouped sparse representation for face recognition

Peng

Liu

et al. 2018

Multimed Tools Appl

Self Cite

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“…, , , h P X D B is a variation of the Fisher discriminant criterion, which is effective for discovering the discriminant of geometric structure and increasing the separability of reconstruction residues [19], and is defined as ( ) ( )…”

Section: Learn P With Fixed Bmentioning

confidence: 99%

“…Oriented the unsupervised dimensionality reduction method, Zhang et al proposed [18] a sparse representation-based classifier (DR-SRC) which has better performance than Eigenfaces and random faces under the same dimensionality. Zhang et al [19] designed a supervised dimensionality reduction method (SDR-SRC) and applied it to face recognition. The SDR-SRC method utilizes a variant LDA to improve the separability between object class and other classes in the stage of updating the projection matrix, which is different from the DR-SRC method.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on SRC Combined with Sparse Embedding Dimensionality Reduction

Yan¹,

Yan²

2022

Journal of Compurters

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<p>Sparse representation-based classification (SRC) method has achieved good recognition results and shown strong robustness for face recognition, especially when the face image is affected by illumination variations, expression changes and occlusion. SRC method simply uses the training set as a dictionary to encode test samples. However, the high-dimensional training face data usually contain a large amount of redundant information, which will increase the complexity of this method. Therefore, the image dimensionality reduction procedure is separately performed by most of the existing methods before SRC is launched, but this may not be able to make full use of the discriminative information of the training samples. In this paper, based on the efficient SRC method, a sparse embedding dimensionality reduction strategy is combined with to achieve a face recognition method. For the proposed method, a projection matrix is used to project high-dimensional data into a low-dimensional space. At the same time, a discriminative coefficient constraint term in the objective function is introduced to reduce the classification residual of the sample through the distance relationship between all coefficients. Then the label information of the sample is used to iteratively update the projection matrix and coefficient representation. Finally, the test samples are projected into the low-dimensional space for classification. A large number of experimental results on three widely used face datasets show that the proposed method improves the discrimination of face images in low-dimensional space and can achieve better face recognition results.</p> <p> </p>

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“…Naturally, Face recognition becomes the typically successful application of supervised NMF. Discriminative NMFs [46,47,69] are the earlier successful attempts of supervised NMF methods at face recognition, and then, many direct NMF methods [35][36][37]70] also demonstrated superior performance in this task.…”

Section: Computer Visionmentioning

confidence: 99%

Recent Advances in Supervised Dimension Reduction: A Survey

Chao

Luo

Ding

2019

MAKE

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Recently, we have witnessed an explosive growth in both the quantity and dimension of data generated, which aggravates the high dimensionality challenge in tasks such as predictive modeling and decision support. Up to now, a large amount of unsupervised dimension reduction methods have been proposed and studied. However, there is no specific review focusing on the supervised dimension reduction problem. Most studies performed classification or regression after unsupervised dimension reduction methods. However, we recognize the following advantages if learning the low-dimensional representation and the classification/regression model simultaneously: high accuracy and effective representation. Considering classification or regression as being the main goal of dimension reduction, the purpose of this paper is to summarize and organize the current developments in the field into three main classes: PCA-based, Non-negative Matrix Factorization (NMF)-based, and manifold-based supervised dimension reduction methods, as well as provide elaborated discussions on their advantages and disadvantages. Moreover, we outline a dozen open problems that can be further explored to advance the development of this topic.

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A supervised dimensionality reduction method-based sparse representation for face recognition

Cited by 10 publications

References 27 publications

A novel grouped sparse representation for face recognition

A novel grouped sparse representation for face recognition

Face Recognition Based on SRC Combined with Sparse Embedding Dimensionality Reduction

Recent Advances in Supervised Dimension Reduction: A Survey

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