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Eigenface Algorithm Based Facial Recognition

Author(s):

Sneha Arora , CBS GROUP OF INSTITUTIONS,JHAJJAR AFFILIATED TO MDU ROHTAK; Rajiv Munjal, CBS GROUP OF INSTITUTIONS,JHAJJAR AFFILIATED TO MDU ROHTAK

Keywords:

Eigenfaces, PCA, Eigen vectors Eigenvalues, training sets, feature vector.

Abstract

Eigenface approach was developed by Sirovich and Kirchy in 1987 and later on Marthew and Alex work on it and used in facial recognition. Eigenfaces thinks image set as 2-D and assumes that at the time of recognition the face of the image should be kept straight and frontal.For this eigenfaces are calculated using principal component analysis . For this we calculate eigenvectors and eigenvalues. To calculate eigenvectors and values we need to have training sets of images which then differentiate input signals from noisy signals. After calculating eigenvectors we chose component and form a feature vector. Eigenvectors with highest eigenvalues is chosen as principal component of data set and then we get ghost like images which we called as eigenfaces.

Other Details

Paper ID: IJSRDV2I3582
Published in: Volume : 2, Issue : 3
Publication Date: 01/06/2014
Page(s): 1499-1500

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