scholarly journals Algorithm of Face Recognition by Principal Component Analysis

1970 ◽  
Vol 3 (2) ◽  
Author(s):  
Khalid A. S. Al-Khateeb and Jaiz A. Y. Johari
Keyword(s):  
Computer Vision ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Data Base ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Algorithm ◽  
Computer Vision Applications ◽  
Simplified Algorithm ◽  
Facial Images

A face recognition algorithm based on Principal Component Analysis (PCA) has been developed and tested for computer vision applications. A database of about 400 facial images was used to test the algorithm. Each image is represented by a matrix (112 x 92), The data base is divided into subsets, where each subset represents one of 10 different individuals. A 96% rate of successful detection and a 90% rate of successful recognition were obtained. Several factors had to be standardized to provide a constrained environment in order to reduce error. The analysis is based on a set of eigenvectors that defines an Eigen Face (EF). The method proved to be simple and effective. The simplified algorithm and techniques expedited the process without seriously compromising the accuracy.

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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