An Experimental Comparison of Dimensionality Reduction for Face Verification Methods

Two different approaches to dimensionality reduction techniques are analysed and evaluated, Locally Linear Embedding and a modification of Nonparametric Discriminant Analysis. Both are considered in order to be used in a face verification problem, as a previous step to nearest neighbor classification. LLE is focused in reducing the dimensionality of the space finding the nonlinear manifold underlying the data, while the goal of NDA is to find the most discriminative linear features of the input data that improve the classification rate (without making any prior assumption on the distribution).