MMPCANet: An Improved PCANet for Occluded Face Recognition

Wang, Zewei; Zhang, Yongjun; Pan, Chengchang; Cui, Zhongwei

doi:10.3390/app12063144

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…PLDANet [33] is trying to combine the PCA filters and LDA filters as they thought that the noises may interfere with the LDA learning process. MMPCANet [34] is a multi-scale multi feature fusion PCANet which is used for occluded face recognition.…”

Section: Introductionmentioning

confidence: 99%

Global and Local Structure Network for Image Classification

2023

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Principal Component Analysis Network (PCANet) is a feature learning algorithm which is widely used in face recognition and object classification. However, original PCANet still has some shortages. One is that PCA algorithm only extracts features by considering the global structure. The other lies in that the original PCANet only employs one particular single layer convolutional results, which loses the information of other convolutional layers. In this paper, we propose a new simple and efficient convolutional neural network called global and local structure network (GLSNet) to address the problems. The network extracts the features both from the global structure and the local structure of the original data space. Specifically, a principal component analysis (PCA) convolutional layer which learns the filters by PCA algorithm is used to remove the noises and redundant information at the first stage. Then at the second stage, another PCA convolution is added to extract features by considering the global structure. As for the local structure, we use the neighborhood preserving embedding (NPE) algorithm to learn the convolutional filters. At the output stage, the global structure feature extracted by PCA convolution and the local structure feature extracted by NPE convolution is concatenated as a united feature. Furthermore, the first layer convolutional feature is also taken into consideration to obtain shallow-level information. Finally, these features are concatenated as a united feature, and a spatial pyramid pooling layer is followed to pool above the united features. To test the effectiveness of the proposed algorithm, the experiments on some image datasets, including three types: human face dataset, object dataset, and handprinted dataset, proceeded. And it performs better than the original PCANet and some improvement algorithms of PCANet, such as PLDANet, and MMPCANet.

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Section: Introductionmentioning

confidence: 99%

Global and Local Structure Network for Image Classification

2023

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“…Wang et al [21] added another PCA convolution in the second stage to extract features by considering the global structure. Wang et al [22] put forward a MMPCANet to obtain more image feature information by using spatial pyramids as the feature pooling layer. Duan et al [23] provided a new multi-scale stack sparse PCANet (MS-SSPCANet) that introduces sparsity, multi-scale filters, and multi-scale pooling layers to optimize the PCANet structure.…”

Section: Introductionmentioning

confidence: 99%

A Bi-Directional Two-Dimensional Deep Subspace Learning Network with Sparse Representation for Object Recognition

Li,

Feng,

Wang

et al. 2023

Electronics

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A principal component analysis network (PCANet), as one of the representative deep subspace learning networks, utilizes principal component analysis (PCA) to learn filters that represent the dominant structural features of objects. However, the filters used in PCANet are linear combinations of all the original variables and contain complex and redundant principal components, which hinders the interpretability of the results. To address this problem, we introduce sparse constraints into a subspace learning network and propose three sparse bi-directional two-dimensional PCANet algorithms, including sparse row 2D2PCANet (SR2D2PCANet), sparse column 2D2PCANet (SC2D2PCANet), and sparse row–column 2D2PCANet (SRC2D2PCANet). These algorithms perform sparse operations on the projection matrices in the row, column, and row–column direction, respectively. Sparsity is achieved by utilizing the elastic net to shrink the loads of the non-primary elements in the principal components to zero and to reduce the redundancy in the projection matrices, thus improving the learning efficiency of the networks. Finally, a variety of experimental results on ORL, COIL-100, NEC, and AR datasets demonstrate that the proposed algorithms learn filters with more discriminative information and outperform other subspace learning networks and traditional deep learning networks in terms of classification and run-time performance, especially for less sample learning.

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A survey on disguise face recognition

Darshan,

Nagasundara

2024

Journal of the Chinese Institute of Engineers

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MMPCANet: An Improved PCANet for Occluded Face Recognition

Cited by 4 publications

References 31 publications

Global and Local Structure Network for Image Classification

Global and Local Structure Network for Image Classification

A Bi-Directional Two-Dimensional Deep Subspace Learning Network with Sparse Representation for Object Recognition

A survey on disguise face recognition

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