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.

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