Discriminant Feature Extraction Based on Center Distance

In this paper, a novel discriminant feature extraction algorithm employing center-based distance is proposed for face recognition. This new method, which is a supervised linear dimensionality reduction and feature extraction approach, computes the center-based distance between each training sample-pairs in the same class and the distance between each training sample-pair belonging to different classes. Then the high-dimensional data are embedded into a low-dimensional space, preserving the within-class geometric structure on a submanifold via maximum variance projection. Many experiments on ORL and Yale face database indicate that this method is highly effective.