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Project: Eigenfaces and Fisherfaces

Table of Contents
Developersonots
First Edition05/2006
Last Modified10/2007
LanguageMatlab

Abstract

This project describes a study of two traditional face recognition methods, the Eigenface [10] and the Fisherface [7]. The Eigenface is the first method considered as a successful technique of face recognition. The Eigenface method uses Principal Component Analysis (PCA) to linearly project the image space to a low dimensional feature space. The Fisherface method is an enhancement of the Eigenface method that it uses Fisher’s Linear Discriminant Analysis (FLDA or LDA) for the dimensionality reduction. The LDA maximizes the ratio of between-class scatter to that of within-class scatter, therefore, it works better than PCA for purpose of discrimination. The Fisherface is especially useful when facial images have large variations in illumination and facial expression. In this project, a comparison of the Eigenface and the Fisherface methods respect to facial images having large illumination variations is examined.

eigenface01.pngeigenface02.pngeigenface03.pngeigenface04.png

The report is available at fileEigenFisherFace.pdf

Tag: Scientific ComputerVision Face Recognition Matlab

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References