Face recognition using Principle Component Analysis and Linear Discriminant Analysis

Abstract

Face recognition is the process of identification of a person by their facial images. This technique makes it possible to use the facial image of a person to authenticate him into a secure system. Face is the main part of human being to be distinguished from one another. Face recognition system mainly takes an image as an input and compares this image with a number of images stored in database to identify whether the input image is in that database or not. There are many techniques used for face recognition. In this paper, we have discussed two techniques: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Both of these techniques are linear. PCA applies linear projection to the original image space to achieve dimensionality reduction. LDA applies linear projection from the image space to a low dimensional space by maximizing the between class scatter and minimizing the within class scatter. These methods will be discussed here based on accuracy and percentage of correct recognition.