Dimensionality Reduction for Face Recognition Using Principal Component Analysis Based Big Bang–Big Crunch Optimization Algorithm

2021 ◽  
pp. 949-955
Author(s):  
Supreet Grewal ◽  
C. Rama Krishna
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Dimensionality Reduction ◽  
Optimization Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Big Bang ◽  
Big Crunch

scholarly journals Feature selected based on PCA and optimized LMC

2021 ◽  
Vol 336 ◽  
pp. 06034
Author(s):  
Ke Xi ◽  
Cheng Cai
Keyword(s):  
Principal Component Analysis ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Optimization Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Large Margin ◽  
Feature Vectors

In this article, we propose an optimization algorithm for the original LMC [1] (Large Margin Classifier). We use PCA [2] (Principal Component Analysis) to reduce the dimensionality of the images, and then put the data after dimensionality reduction into the optimized LMC for the feature selection [3]. We will get several features with the greatest distinction. We use these features to classify images. Finally, the experiment shows that the accuracy of the optimized LMC under the same dimensions is higher than that of the original LMC, and in many cases, the accuracy of the optimized LMC after taking 6 feature vectors has exceeded the highest accuracy of the original LMC.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

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