Underlying Connections Between Algorithms for Nongreedy LDA-L1

Ye, Qiaolin; Zhao, Henghao; Fu, Liyong; Gao, Shanbing

doi:10.1109/tip.2018.2803305

Cited by 10 publications

(3 citation statements)

References 3 publications

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“…Inspired by the L1-normbased algorithms, several dimensionality reduction methods have been exploited. For instance, the most representative are LDA-L1 [23], 2DLDA-L1 [24], 2DPCA-L1 [25], and 2DCRP-L1 [26].…”

Section: Related Workmentioning

confidence: 99%

DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

et al. 2021

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In this paper, we propose a robust face recognition model called DeepWTPCA-L 1 using WTPCA-L 1 features and a CNN-LSTM architecture. First, WTPCA-L 1 algorithm, composed of Threelevel decomposition of discrete wavelet transform followed by PCA-L 1 algorithm, is exploited to extract face features. Then, the extracted features are used as inputs of the proposed CNN-LSTM architecture.To evaluate the robustness of the proposed approach, several face recognition datasets have been used. In addition, the proposed method is trained on noisy images using Gaussian, and Salt & Pepper noise added to the facial images of each dataset. The results of the experiment indicate that the proposed model achieves high recognition performance on three well-known standard face databases. When compared to state-ofthe-art methods, the proposed model achieves a better face recognition rate.

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Section: Related Workmentioning

confidence: 99%

DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

et al. 2021

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“…或其局部的表面特性 [1] , 其被广泛地用作人脸特征描述符, 以区分和检索图像 [2][3] . 全局纹理描述符可以刻画人脸的粗略信息, 具有代表性的方法有主成分分析法 (principal component analysis, PCA) [4] 和线性判别分析法 (linear discriminant analysis, LDA) [5] 等. 而局部纹理描述符更能反映人脸图像鉴别信息的细节部分, 通常它对光照和表情等变化因素具有较强的鲁棒性.…”

Section: 人脸识别大多数研究中都使用了纹理分析和分类纹理反映了图像的视觉特征表征一个物体unclassified

Face Recognition with Local High-Order Principal Direction Pattern Based on “Gradient Face”

Ye¹,

Wang²,

Ying³

et al. 2021

J Comput-Aided Design Comput Graphics

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Pointing to weak robustness caused by the noise sensitivity and feature redundancy of present face recognition methods with high-order features, a new method of the local high-order principal direction pattern based on "gradient face" is proposed. Firstly, the gradient face convolution operator designed is used to compute the sum of multi-directional gradient components of pixels to construct a gradient face. Then, the principal direction grouping strategy is introduced on the gradient face to characterize its high-order derivative features, and a principal direction feature map is formed according to the feature code of high-order derivatives direction changes in local neighborhood. Finally, block statistics and cascading of histogram features are made a vector to be input in to a support vector machine for multi-classification. Experimental results of several public face databases show that the proposed method is robust to changes in illumination, expression, and facial occlusion and has higher recognition efficiency.

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“…Motivated by the aforementioned l 1 -norm-based methods, many l 1 -norm-based techniques have been further promoted for data dimensionality reduction and image recognition. Two of the most representative methods are LDA-L1 [26], [27] and 2DPCA-L1 [28], [29]. Moreover, to broaden the applicability of the l 1 -norm, the l 1 -norm has been extended to the l p -norm, and a series of l p -norm-based methods have been proposed [30]- [36].…”

Section: Introductionmentioning

confidence: 99%

Generalized Robust PCA: A New Distance Metric Method for Underwater Target Recognition

et al. 2019

IEEE Access

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Inspired by the importance of distance metrics and the structure-preserving ability of features, a novel recognition method for underwater targets, called generalized robust principal component analysis (GRPCA), is proposed in this paper. Several advantages of GRPCA are summarized as follows. First, GRPCA employs the l 2,p -norm as the distance metric for calculating the reconstruction error and variance of projected data and attempts to minimize the sum of the ratios between the reconstruction error and the variance for each data sample. This approach allows it to extract the feature information of an image more accurately, which is important for recognition and representation. Second, the proposed GRPCA algorithm not only is robust but also retains the desirable properties of PCA, such as rotational invariance. Moreover, we present a simple yet efficient iterative update algorithm to solve the GRPCA problem. Finally, on the basis of GRPCA, underwater target recognition technology is developed. The extensive experiments on several underwater optical image databases show that our method is more effective and advantageous than other subspace learning algorithms are.INDEX TERMS Distance metric, generalized robust principal component analysis (GRPCA), underwater target recognition.

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Underlying Connections Between Algorithms for Nongreedy LDA-L1

Cited by 10 publications

References 3 publications

DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

Face Recognition with Local High-Order Principal Direction Pattern Based on “Gradient Face”

Generalized Robust PCA: A New Distance Metric Method for Underwater Target Recognition

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Underlying Connections Between Algorithms for Nongreedy LDA-L1

Cited by 10 publications

References 3 publications

DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

Face Recognition with Local High-Order Principal Direction Pattern Based on &ldquo;Gradient Face&rdquo;

Generalized Robust PCA: A New Distance Metric Method for Underwater Target Recognition

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Face Recognition with Local High-Order Principal Direction Pattern Based on “Gradient Face”