Principal manifolds and probabilistic subspaces for visual recognition

We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and nonlinear Kernel PCA (KPCA) are examined and tested in a visual recognition experiment using 1,800+ facial images from the FERET database. We compare the recognition performance of nearest-neighbor matching with each principal manifold representation to that of a maximum a posteriori (MAP) matching rule using a Bayesian similarity measure derived from dual probabilistic subspaces. The experimental results demonstrate the simplicity, computational economy, and performance superiority of the Bayesian subspace method over principal manifold techniques for visual matching.