Principal Manifolds and Bayesian Subspaces for Visual Recognition
Where Published: Proceedings of the 7th IEEE International Conference on Computer Vision, ICCV'99, September, 1999
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.
