Image Processing Using Principal Component Analysis

Abstract

In this paper, a review on the latest methodologies and application of the Principle Component Analysis (PCA) has been done in the area of image processing. Exploring basic theory of multivariate analysis, which involves a mathematical procedure to transform a number of correlated variables into a number of uncorrelated variables have been studied, compared and analyzed for better performance. The PCA ultimately reduces the number of effective variables used for classification which are compared with some statistical method. A comparison is made to illustrate the important of PCA in various signal processing based application like Texture classification, Face recognition, Biometrics etc.