Principal manifolds and Bayesian subspaces for visual recognition

We investigate the use of linear and nonlinear principal manifolds for learning low dimensional representations for visual recognition. Three techniques: principal component analysis (PCA), independent component analysis (ICA) and nonlinear PCA (NLPCA) are examined and tested in a visual recognition experiment using a large gallery of facial images from the "FERET" database. We compare the recognition performance of a nearest neighbour matching rule with each principal manifold representation to that of a maximum a posteriori (MAP) matching rule using a Bayesian similarity measure derived from probabilistic subspaces, and demonstrate the superiority of the latter.