Multiplication fusion of sparse and collaborative representation for robust face recognition

Zeng, Shaoning; Yang, Xiong; Gou, Jianping

doi:10.1007/s11042-016-4035-5

Cited by 25 publications

(16 citation statements)

References 27 publications

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“…The main concept of the representation classifier [24], [25] is to express the test samples with an overcomplete dictionary linearity composed of a training set. We assume that there are c classes in which the number of the cth training sample is n c , and the total number of training samples is…”

Section: Related Work a Sparse Representationmentioning

confidence: 99%

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

Cheng

2020

IEEE Access

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The representation-based classification method has become a research hotspot in recent years. Representation-based classifiers assign class labels directly to test samples based on a structured dictionary. The structured dictionary is composed of training samples. We call the samples in the dictionary as atoms. To further improve the expression ability of different training atoms in the classifier to the test samples, we propose an ensemble-enhanced sparse classification algorithm based on the correlation coefficient. The model proceeds from two aspects: the representation coefficient level and the similarity between different samples. First, a sparse dictionary is combined with the multiplicative property of training samples to build and solve the sparse and collaborative representation algorithm. The fusion representation coefficients are obtained by weighted sparse representation coefficients and collaborative representation coefficients. The test samples are reconstructed by fusing the representation coefficients, and the minimization recovery residuals for each class of samples are calculated. Second, the correlation coefficient value between the test sample and the training sample is calculated, and the maximum correlation quotient between the test sample and the training sample is obtained. Finally, the maximum correlation quotient and the minimization recovery residual are weighted, and the decision classification is carried out in the decision function to achieve the final face recognition. Experiments on AR, Extended Yale B, Georgia Tech and other general face databases show the effectiveness of the algorithm. The main contribution of this model is that it is more robust than a single sparse or collaborative representation model and can improve recognition accuracy.

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Section: Related Work a Sparse Representationmentioning

confidence: 99%

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

Cheng

2020

IEEE Access

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“…Obviously, several regions are sparser in NCRC compared with CRC (237 coefficients from NCRC are 0). As the sparsity of collaborative representation can help the classifier to perform more robust classification [20,23], the recognition performance of NCRC is better than the original CRC (see Section 3.2, Section 3.3, Section 3.4 and Section 3.5).…”

Section: Crc and Non-negative Crcmentioning

confidence: 99%

“…For instance, Dong et al used sparse subspace on weighted CRC [14] to improve the recognition rate for face recognition [19]. Zeng et al proposed S*CRC to achieve promising performances by fusing coefficients from sparse representation with the coefficients from collaborative representation [20]. However, in this work, each test sample is represented twice using SRC and CRC simultaneously, which is time consuming.…”

Section: Introductionmentioning

confidence: 99%

“…Zheng et al selected k candidate classes before representing a test sample collaboratively, while the p in the objective function should be defined in advance to obtain optimal results [21]. In [14,18,19,20,21], the authors ignored the relationship between the collaborative coefficient and the test sample. However, we strongly believe that, by considering this relationship, we are able to obtain a higher recognition rate.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative Representation Using Non-Negative Samples for Image Classification

Zhou

Zhang

2019

Sensors

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Collaborative representation based classification (CRC) is an efficient classifier in image classification. By using l 2 regularization, the collaborative representation based classifier holds competitive performances compared with the sparse representation based classifier using less computational time. However, each of the elements calculated from the training samples are utilized for representation without selection, which can lead to poor performances in some classification tasks. To resolve this issue, in this paper, we propose a novel collaborative representation by directly using non-negative representations to represent a test sample collaboratively, termed Non-negative Collaborative Representation-based Classifier (NCRC). To collect all non-negative collaborative representations, we introduce a Rectified Linear Unit (ReLU) function to perform filtering on the coefficients obtained by l 2 minimization according to CRC’s objective function. Next, we represent the test sample by using a linear combination of these representations. Lastly, the nearest subspace classifier is used to perform classification on the test samples. The experiments performed on four different databases including face and palmprint showed the promising results of the proposed method. Accuracy comparisons with other state-of-art sparse representation-based classifiers demonstrated the effectiveness of NCRC at image classification. In addition, the proposed NCRC consumes less computational time, further illustrating the efficiency of NCRC.

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“…Moreover, such two-phase collaborative representation-based classification also has the property of sparsity for enhancing the ability of pattern discrimination [30]. Using the superiorities of sparse representation and collaborative representation, the extensions of combining both were proposed for classification in [34,35,37,38]. Besides, due to good latent discrimination contained in the representation, sparse representation and collaborative representation were utilized to design the effective nearest neighbor classification [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Discriminative, Competitive, and Collaborative Representation‐Based Classification with l₂‐Norm Regularizations

et al. 2019

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Recently, collaborative representation-based classification (CRC) and its many variations have been widely applied for various classification tasks in pattern recognition. To further enhance the pattern discrimination of CRC, in this article we propose a novel extension of CRC, entitled discriminative, competitive, and collaborative representation-based classification (DCCRC). In the proposed DCCRC, the class discrimination information is fully utilized for promoting the true class of each testing sample to dominantly represent the testing sample during collaborative representation. The class discrimination information is well considered in the newly designed discriminative l2-norm regularization that can decrease the ability of representation from the interclasses of each testing sample. Simultaneously, a competitive l2-norm regularization is introduced to the DCCRC model with the class discrimination information with the aim of enhancing the competitive ability of representation from the true class of each testing sample. The effectiveness of the proposed DCCRC is explored by extensive experiments on the several public face databases and some real numerical UCI data sets. The experimental results demonstrate that the proposed DCCRC achieves the superior performance over the state-of-the-art representation-based classification methods.

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Multiplication fusion of sparse and collaborative representation for robust face recognition

Cited by 25 publications

References 27 publications

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

Collaborative Representation Using Non-Negative Samples for Image Classification

Discriminative, Competitive, and Collaborative Representation‐Based Classification with l₂‐Norm Regularizations

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Multiplication fusion of sparse and collaborative representation for robust face recognition

Cited by 25 publications

References 27 publications

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

Collaborative Representation Using Non-Negative Samples for Image Classification

Discriminative, Competitive, and Collaborative Representation‐Based Classification with l2‐Norm Regularizations

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Product

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Discriminative, Competitive, and Collaborative Representation‐Based Classification with l₂‐Norm Regularizations