Principal Manifolds and Bayesian Subspaces for Visual Recognition

Authors:
Baback Moghaddam
Affiliations:
-
Venue:
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Year:
1999

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Abstract

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-neighbor 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.