Research Journal of Applied Sciences
ISSN: Online 1993-6079ISSN: Print 1815-932x
Abstract
By using the direct relationship between the Principle Component Analysis (PCA) and Singular Value Decomposition (SVD), it can draw the important landmarks that represent the basic components of the data, tried to create preference in terms of rates of discrimination within the SVD decomposition matrices themselves. Experimentally, it have been found out that high percentage of similarity between SVD and PCA when applied on the same dataset of images in terms of results. As result, the advantage of the direct relationship between PCA and SVD has been exploited and using SVD’s principle components as features for recognition stage. Least Square Support Vector Machine( LSSVM) has been applied to recognize faces.
How to cite this article:
W. Al-Hameed. An Efficient SVD’s Principle Components for Face Recognition.
DOI: https://doi.org/10.36478/rjasci.2016.948.952
URL: https://www.makhillpublications.co/view-article/1815-932x/rjasci.2016.948.952