Revisiting the Performance of PCA Versus FDA Versus Simple Projection for Image Recognition
By the advancement of technology, people are using internet for interchanging millions of photos every day from one to another part of the world, where data reductions are used to send file long distance with in minimum period of time. In the medical science physicians are detecting body organs, tumor cells and complex physical phenomena by optical fibers and where image processing is quite useful. One of another important sector of image processing is meteorology where it processes satellite sending images to do daily weather forecasting or finding climate change. There are many other sectors such as military surveillances, underwater search, satellite navigation etc. Suppose there are millions of images in the database of NSA, but they do not have clear image of the suspect (or suspects) Their main aim is to find the image (or multiple images) of particular scene and identify object of interest in the image (or images) In this study, we will set a very well known paradigm of analysis using PCA, FDA and simple projection to recognize people from their facial images. We will consider that we have some images of known people that can be used to compare and recognize new images (of the same set of face images) Moreover we will show graphical and tabular representation for average performance of correct recognition as well as analyze the effectiveness of three projections. Keywords SVD, orthogonal matrix, orthogonal linear transformation, orthogonal projection, PCA, FDA Methodology As images are very high dimensional, it is not easy to analyze them directly. Some common approaches are to reduce their diminution using principal component analysis (PCA) Fisher's discriminant analysis (FDA) and other similar methods. Both PCA and LDA are linear transformation methods. PCA yields the directions (principal components) that maximize the variance of the data, whereas LDA also aims to find the directions that maximize the separation