Search

Linear Discriminant Analysis (LDA) has been one of the popular subspace methods for face recognition. But this method suffers from the small sample size (SSS) problem, also known as 'curse of dimensionality'. Various techniques have been proposed in literature to overcome this limitation. But it is still unclear which method provides the best solution to SSS problem. In this paper, we have investigated the performance of some popular subspace methods such as principal component analysis (PCA), PCA + LDA, LDA via QR decomposition, Null-space LDA, Exponential Discriminant Analysis (EDA), PCA+EDA etc. Extensive experiments have been performed on five publically available face datasets viz. AR, CMU-PIE, PIX, Yale and YaleB. The performance is measured in terms of average classification accuracy. Experimental results show that the performance increases with the increase in the number of images per person in training set irrespective of the datasets. There is no clear winner among the subspace methods under investigation. But, the performance of PCA+LDA and PCA+EDA is consistent in tackling SSS problem irrespective of the dataset and can also handle the illumination variation in face recognition.

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.