Comparison of PCA and LDA based face recognition algorithms under illumination variations

Author

Cho, Hyunjong ; Moon, Seungbin

Author_Institution

Dept. of Comput. Eng., Sejong Univ., Seoul, South Korea

fYear

2009

fDate

18-21 Aug. 2009

Firstpage

4025

Lastpage

4030

Abstract

In this paper, we study face recognition using principal component analysis (PCA) and linear discriminant analysis (LDA) under illumination variations. A modified census transform (MCT) is applied as preprocessing step to compensate illumination variations, and then PCA and LDA are employed to find lower-dimensional subspaces for face recognition. Distances between training and testing images are measured by three metrics (L1, L2, and cosine). The aim of this paper is to compare the results of two most popular subspace projection methods under illumination variation conditions.

Keywords

face recognition; principal component analysis; transforms; LDA; PCA; face recognition; illumination variation condition; linear discriminant analysis; lower-dimensional subspace projection; modified census transform; preprocessing step; principal component analysis; Access control; Biometrics; Face recognition; Histograms; Independent component analysis; Lighting; Linear discriminant analysis; Moon; Principal component analysis; Testing; Cumulative Match Characteristic (CMC); Linear Discriminant Analysis (LDA); Principal Component Analysis (PCA); confidence level; face recognition; illumination;

fLanguage

English

Publisher

ieee

Conference_Titel

ICCAS-SICE, 2009

Conference_Location

Fukuoka

Print_ISBN

978-4-907764-34-0

Electronic_ISBN

978-4-907764-33-3

Type

conf

Filename

5333255