scholarly journals Integration of Two-Dimensional Kernel Principal Component Analysis Plus Two-Dimensional Linear Discriminant Analysis with Convolutional Neural Network for Finger Vein Recognition

2021 ◽  
Vol 38 (4) ◽  
pp. 1181-1187
Author(s):  
Zhitao Gao ◽  
Jianxian Cai ◽  
Yanan Shi ◽  
Li Hong ◽  
Fenfen Yan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Two Dimensional ◽  
Linear Discriminant ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

High complexity and low recognition rate are two common problems with the current finger vein recognition methods. To solve these problems, this paper integrates two-dimensional kernel principal component analysis (K2DPCA) plus two-dimensional linear discriminant analysis (2DLDA) (K2DPCA+2DLDA) into convolutional neural network (CNN) to recognize finger veins. Considering the row and column correlations of the finger vein image matrix and the classes of finger vein images, the authors adopted K2DPCA and 2DLDA separately for dimensionality reduction and extraction of nonlinear features in row and column directions, producing a dimensionally reduced compressed image without row or column correlation. Taking the dimensionally reduced compressed image as the input, the CNN was introduced to learn higher-level features, making finger vein recognition more accurate and robust. The public dataset of Finger Vein USM (FV-USM) Database was adopted for experimental verification. The results show that the proposed approach effectively overcome the common defects of original image feature extraction: the insufficient feature description, and the redundancy of information. When the training reached 120 epochs, the model basically realized stable convergence, with the loss approaching zero and the recognition rate reaching 97.3%. Compared with two-directional two-dimensional Fisher principal component analysis ((2D)2FPCA), our strategy, which integrates K2DPCA+2DLDA with CNN, achieved a very high recognition rate of finger vein images.

2D Image Matrix-Based Discriminator

2006 ◽  
pp. 258-286
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Image Projection ◽  
Projection Techniques

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

An Improved Kernel Principal Component Analysis Based on Chaotic Artificial Fish School Algorithm

2013 ◽  
Vol 655-657 ◽  
pp. 931-935
Author(s):  
Fang Min Hu ◽  
Hui Ya Zhao
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Recognition Rate ◽  
Feature Selection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Local Optimum ◽  
Fish School ◽  
Feature Subspace

The feature extraction is a great important step for face recognition. When all features are extracted and selected for face recognition, it results in poor recognition rate because there are too many irrelevant, redundant and noisy features which also increase the time consumption. Therefore, a good feature selection method is necessary. This problem can be regarded as a combinatorial optimization solution. To overcome this problem, An improved kernel principal component analysis based on chaotic artificial fish school algorithm is proposed. The feature subspace of face pictures is obtained by standard kernel principal component analysis where a better feature subspace is selected by improved chaotic artificial fish school algorithm which based on couple chaotic maps increases the diversity of fish, has better global convergence ability and is not easy to fall into local optimum when facing with complex problems. The experimental results show that the proposed method has significantly improved the performance of conventional kernel principal component analysis.

SAR Images Recognition Combined Bidirectional 2DPCA with KPCA

2013 ◽  
Vol 756-759 ◽  
pp. 4045-4049 ◽  
Author(s):  
De Gong Wang ◽  
Yong Li ◽  
Fu Lu Jin
Keyword(s):  
Principal Component Analysis ◽  
Recognition Rate ◽  
Image Data ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Sar Images ◽  
High Order Statistics ◽  
Images Recognition ◽  
Calculated Amount

The bidirectional 2DPCA (two-dimensional principal component analysis) method for SAR images recognition, can compress the columns and rows of images matrix and reduce the number of feature dimensions. However, it fails to use high order statistics information of image data, neglects the nonlinearity correlation between pixels. Therefore, this paper presents the method combined bidirectional 2DPCA with KPCA (Kernel Principal Component Analysis). This method not only compresses the dimensions of images data, but develops the superiority of KPCA in describing correlation between many pixels. Experimental results show that: this method can decrease calculated amount and raise recognition rate of SAR target effectively.

A Finger Vein Recognition Method Based on PCA-RBF Neural Network

2013 ◽  
Vol 325-326 ◽  
pp. 1653-1658 ◽  
Author(s):  
Cheng Bo Yu ◽  
Jun Tan ◽  
Lei Yu ◽  
Yin Li Tian
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Back Propagation ◽  
Principal Component ◽  
Training Sample ◽  
Finger Vein ◽  
Simpler Algorithm ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper puts forward a finger vein classification algorithm which combines Principal Component Analysis (PCA) with Radial Basis Function (RBF) neural network algorithm, named the PCA-RBF algorithm. Use the training sample to reduce PCA dimensions, and abstract the main component of the image. Because of the advantages of RBF neural network classifying, put finger vein images into different classes, and then use the shortest distance to recognize. Through the experiment result comparing with Back Propagation (BP) neural network, PCA-RBF neural network is better in finger vein recognition. The result shows that PCA-RBF has faster training speed, simpler algorithm and higher recognition rate.

Research on athlete skipping surface electromyography and energy consumption based on principal component analysis of wavelet packet

2020 ◽  
pp. 1-11
Author(s):  
Yanan Yu
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Signal Acquisition ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Linear Discriminant ◽  
Emg Signal

EMG signal acquisition is mostly used in medical research. However, it has not been applied in athletes’ sports state recognition and body state detection, and there are few related studies at present. In order to promote the application of EMG signal acquisition in sports, this study combined with the actual needs of athletes to construct an EMG signal acquisition system that can collect athletes’ motion status. At the same time, in order to improve the effect of EMG signal acquisition, a wavelet packet principal component analysis model is proposed. In addition, in order to ensure the recognition efficiency of athletes’ motion state, this paper uses linear discriminant analysis method as the motion recognition assistant algorithm. Finally, this paper judges the performance of this research model by setting up comparative experiments. The research shows that the wavelet packet principal component analysis model performance is significantly better than the traditional algorithm, and the recognition rate for some subtle motions is also high. In addition, this study provides a theoretical reference for the application of EMG signals in the sports industry.

scholarly journals Image Recognition Based on Two-Dimensional Principal Component Analysis Combining with Wavelet Theory and Frame Theory

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Pingping Tao ◽  
Xiaoliang Feng ◽  
Chenglin Wen
Keyword(s):  
Principal Component Analysis ◽  
Image Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Image Features ◽  
Frame Theory ◽  
Sufficient Information ◽  
Two Dimensional ◽  
Wavelet Theory

The key to improve the image recognition rate lies in the extraction of image features. In this paper, a feature extraction method is proposed for the images with similar feature in the strong noise background, which is two-dimensional principal component analysis combined with wavelet theory and frame theory. Considering that the image will be influenced by man-made and environmental noises, the algorithm of this paper considers the improvement of many algorithms. Firstly, the images are preprocessed by images enhancement based on feature enhancement. The images are processed by wavelet transform. Then, the preprocessed image matrices are used to obtain the eigenvectors, and the eigenvectors are interpolated with frame, which makes more sufficient information in the frame theory and better extracts the features on the image. Finally, this algorithm is compared other algorithms in the standard ORL face recognition database. The comparison of recognition rate and recognition time by simulation experiment is carried out in order to obtain the validity of the proposed algorithm.

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