Performance Evaluation of Linear Subspace Methods for Face Recognition under Illumination Variation

Due to high dimensionality of face images and finite number of training samples, the linear subspace technique for face recognition pose challenges for better performance. In this paper, we compare the performance of linear subspace methods which involve the computation of scatter matrices for face recognition under illumination variation. The performance of these methods is evaluated in terms of classification accuracy, computational training and testing time. Extensive empirical experiments are performed to compare the performance using AR, Pie, Yale and YaleB face databases. In absence of sufficient number of training samples, classification accuracy of linear subspace methods deteriorate. Experimental results show that the performance of Dual LDA is best in terms of average classification accuracy. It is also observed that Fisherface takes minimum training time and both ERE and SVM takes minimum testing time. No linear subspace method outperforms others in terms of all performance measures.