31 search results - page 1 / 7 » Principal Manifolds and Bayesian Subspaces for Visual Recogn... |
ICCV
1999
IEEE
13 years 8 months ago
1999
IEEE
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Three techniques: Principal Component Analy...
PAMI
2002
13 years 4 months ago
2002
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
PR
2007
13 years 4 months ago
2007
In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particula...
ICCV
2003
IEEE
14 years 6 months ago
2003
IEEE
Previous works have demonstrated that the face recognition performance can be improved significantly in low dimensional linear subspaces. Conventionally, principal component analy...
IPAS
2010
13 years 2 months ago
2010
In this paper, we consider facial expression recognition using an unsupervised learning framework. Specifically, given a data set composed of a number of facial images of the same...