Fisher discriminant analysis based on kernel cuboid for face recognition

Liu, Xiao-Zhang; Zhang, Chenguang

doi:10.1007/s00500-015-1794-2

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…If M 1 , N 1 , and P 1 are connected counterclockwise, we establish a 2D coordinate system X O Y as shown in Figure 7, p i and q i mean the corresponding lengths of 2D coordinates. The coordinate value of the i-th 2D T i is as shown in (14).…”

Section: Side Of the Oriented Bounding Boxmentioning

confidence: 99%

“…From the set of nonlinear algorithms that retain the global features, several algorithms use kernel technology such as Kernel Principal Component Analysis [12] and Kernel Fisher Discriminant Analysis [13], which can be seen as nonlinear versions of classic algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Another type of dimensionality reduction algorithms is based on distance retention and includes Multiple Dimensional Scaling [14] (MDS) which keeps the distance between the original spatial samples and the distance in the lowdimensional space as much as possible, Isometric Mapping [15] (Isomap) which is a non-iterative globally optimized dimensionality reduction algorithm that requires the geodesic distance to remain constant before and after dimensional reduction, and Diffusion Maps [16] which is dimensionally reduced by diffusion processes to remove redundant information.…”

Section: Introductionmentioning

confidence: 99%

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Dimensionality Reduction Method for 3D-Handwritten Characters Based on Oriented Bounding Boxes

Wang

Zhao

et al. 2020

IEEE Access

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Dimensionality reduction of 3D-handwritten characters can be problematic because of random mirror rotations and angle rotations which appear after using the traditional algorithms. In order to overcome the above drawbacks of the traditional methods, this study proposes a new algorithm for dimensionality reduction of 3D-handwritten characters based on oriented bounding boxes. First, we get a 3D discrete point set T and generate a 3D trajectory. Then, we apply an oriented bounding box model and determine the projection surface. Next, we perform three coordinate transformations, including (1) pre-transforming the 3D discrete point set T into the projection point set T 1 , (2) converting T 1 to a two-dimensional point set T 2 which has solved the problem of mirror rotation, and (3) converting T 2 to a dimensionally reduced point set T 3 which has solved the problem of angle rotation. Finally, we obtain a dimensionally reduced image without mirror and angle rotations. The experimental results confirm that the proposed method can not only obtain a better visual dimensionally reduced image, but also has a higher recognition rate than the conventional ones. INDEX TERMS 3D-handwritten character recognition, dimension reduction, oriented bounding box.

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Section: Side Of the Oriented Bounding Boxmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction Method for 3D-Handwritten Characters Based on Oriented Bounding Boxes

Wang

Zhao

et al. 2020

IEEE Access

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“…With the rapid development of computer science and technology, multivariable statistical process monitoring (MSPM) has become the primary tool for application in this field [1][2][3][4][5][6][7][8]. MSPM, such as principal comp onent analysis (PCA) [9], partial least squares [10], indepen dent component analysis [11] and Fisher discriminant analysis [12], is suitable for massive data, and its extensions have been extensively applied to chemical production processes. In addi tion, machinelearningbased methods, such as the knearest neighbor and support vector machine, perform well in fault detection [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Industrial process deep feature representation by regularization strategy autoencoders for process monitoring

Kong

Yan

2019

Meas. Sci. Technol.

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Autoencoders and stacked autoencoders (SAEs) are efficient for detecting abnormal situations in process monitoring because of their powerful deep feature representation capability. However, SAEs are easy to overfit during training, thereby affecting this representation. Furthermore, several nodes of the same layer in the SAE carry duplicate information, and thus the features are strongly correlated. To solve these problems, a novel regularization strategy, in which the inner product is introduced, is proposed for the SAE to reduce overfitting more effectively. The modified SAE is called an inner product-based stacked autoencoder (IPSAE). SAEs aim to reduce the Euclidean distance between the output and input matrices through iterative calculation, whereas the IPSAE adds the inner products between the outputs of the neurons to the objective function to regularize the features and reduce feature redundancy. Hence, after determining the structure of the SAE, it is trained to lower the reconstruction error and inner product between the outputs of the neurons to improve the deep feature representation of the industrial process. The proposed model is applied to a numerical system and a Tennessee Eastman dataset, and demonstrates the best performance when compared with several state-of-the-art models.

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“…Many dimensionality reduction methods have been proposed, and most of them have been successfully applied to facerecognition problems [1][2][3][4]. The most popular algorithms are principal component analysis (PCA) [5], linear discriminant analysis (LDA) [6], and independent component analysis [7]. Recently, many methods are proposed to improve the above traditional dimensionality reduction methods.…”

Section: Introductionmentioning

confidence: 99%

Application of MEEMD in post‐processing of dimensionality reduction methods for face recognition

et al. 2018

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Dimensionality reduction techniques are powerful tools for face recognition, because they obtain important information from a dataset. Several dimensionality reduction methods proposed in literature have been improved thanks to preprocessing approaches. However, they also require post-processing to rectify and increase the quality of projected data. This study presents a simple and new discriminative post-processing framework to make the dimensionality reduction methods robust to outliers. In detail, the proposed approach separates features according to their scale using multidimensional ensemble empirical mode decomposition (MEEMD) and then the spatial and frequency domain processing methods are employed to preserve crucial features. The performance of the proposed method is evaluated on ORL, Extended Yale B, AR, and LFW datasets by several dimensionality reduction techniques. The experimental results demonstrate that the proposed algorithm can perform very well in face recognition.

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Fisher discriminant analysis based on kernel cuboid for face recognition

Cited by 5 publications

References 28 publications

Dimensionality Reduction Method for 3D-Handwritten Characters Based on Oriented Bounding Boxes

Dimensionality Reduction Method for 3D-Handwritten Characters Based on Oriented Bounding Boxes

Industrial process deep feature representation by regularization strategy autoencoders for process monitoring

Application of MEEMD in post‐processing of dimensionality reduction methods for face recognition

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