A Comparative Study of Linear Discriminant and Linear Regression Based Methods for Expression Invariant Face Recognition

Abstract

In the literature, the performance of Fisher’s Linear Discriminant (FLD), Linear Regression (LR) and their variants is found to be satisfactory for face recognition under illumination variation. However, face recognition under expression variation is also a challenging problem and has received little attention. To determine suitable method for expression invariant face recognition, in this paper, we have investigated several methods which are variants of FLD or LR. Extensive experiments are performed on three publicly available datasets namely ORL, JAFFE and FEEDTUM with varying number of training images per person. The performance is evaluated in terms of average classification accuracy. Experimental results demonstrate superior performance of Enhanced FLD (EFLD) method in comparison to other methods on all the three datasets. Statistical ranking used for comparison of methods strengthen the empirical findings.