scholarly journals FACE RECOGNITION IN NEUROSCIENCE: A REMEDY FOR PROSOPAGNOSIA AFFECTED PEOPLE

2013 ◽  
pp. 216-219
Author(s):  
N. ASHITHA ◽  
ADITI. A ◽  
K. RAMESH KAMATH
Keyword(s):  
Principal Component Analysis ◽  
Image Analysis ◽  
Face Recognition ◽  
Face Perception ◽  
Principal Component ◽  
Component Analysis ◽  
Automated System ◽  
Human Ability ◽  
Successful Approach

Face recognition is one of the most relevant applications of image analysis. It’s a true challenge to build an automated system which equals human ability to recognize faces. In the topic presented below the focus is on application of face recognition in Neuroscience, to help people affected with Prosopagnosia. Prosopagnosia is a disorder of face perception where the ability of a patient to recognize faces is impaired, while the ability to recognize other objects may be relatively intact.There are many algorithms proposed for face recognition. The most successful approach of those are the extensions of the principal component analysis.

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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