Random sampling LDA incorporating feature selection for face recognition

Classical Linear Discriminant Analysis (LDA) is usually suffers from the small sample size (SSS) problem when dealing with the high dimensional face data. Many methods have been proposed for solving this problem such as Fisherface and Null Space LDA (N-LDA), but these methods are overfitted to the training set and inevitably lose some useful discriminative information in many cases. To effectively utilize nearly all useful discriminative information, a not completely random sampling framework for the integration of multiple features is developed. However, this method has the following main disadvantage: By directly employing feature extraction, the newly constructed variables may contain lots of information originated from those redundant features in the original space. So, in this paper, we introduce a new random sampling LDA by incorporating feature selection for face recognition, that is, some redundant features are removed using the given feature selection methods at first, and then PCA is employed, finally we use random sampling to generate multiple feature subsets. Along this, corresponding weak LDA classifiers are naturally generated and an integrated classifier is developed using a fusion rule. Experiments on 4 face datasets (AR, ORL, Yale, YaleB) show the effectiveness of our algorithm.