Effect Of Eigenfaces Level On The Face Recognition Rate Using Principal Component Analysis


This paper presents an approach to study the effect of the different eigenfaces levels on the faces recognition rate using principal component analysis. The increase in the strength of the variables and the lighting in the facial geometry to represent the human face , has been using the principal component analysis (PCA) on the image of the whole face . The principal component analysis is a statistical measurement method , which works in the field of linear and can be used to reduce the dimensions of the image and thus serve to reduce the calculations significantly to the image database . It is a method gives better accuracy and a higher rate of recognition . The experiment was conducted on 50 images from the database of faces (ORL), using 40 images for the training set and 15 images for the test group ( five images in common with the training set and the remaining 10 images are different in expression and corner ) . The results proved that the proposed method is effective and successful in obtaining recognition rate up to 100% in the third level when using ten eigenfaces.