Title :
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
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;
Conference_Titel :
ICCAS-SICE, 2009
Conference_Location :
Fukuoka
Print_ISBN :
978-4-907764-34-0
Electronic_ISBN :
978-4-907764-33-3
