Illumination Invariant Face Recognition Using Principal Component Analysis-An Overview

Abstract

Illumination variation is a challenge problem at face recognition since a face image varies as illumination changes. In this paper, it is reviewed the illumination variation methods in the state-of-the-art such as the single scale retinex algorithm, the multi scale retinex algorithm, the gradientfaces based normalization method, the Tan and Triggs normalization method and the single scale weberfaces normalization method. The face recognition is performed by using Principal Component Analysis (PCA) in MATLAB environment. AR face database is used for evaluating the face recognition algorithm using PCA. The distance classifier called as Squared Euclidean is used. Experimental results are comparatively demonstrated. © Springer-Verlag Berlin Heidelberg 2015.