Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small.
History
Event
IEEE Conference on Computer Vision and Pattern Recognition (26th : 2008 : Anchorage, Alaska)
Pagination
1 - 7
Publisher
IEEE
Location
Anchorage, Alaska
Place of publication
Washington, D. C.
Start date
2008-06-23
End date
2008-06-28
ISBN-13
9781424422425
ISBN-10
1424422426
Language
eng
Publication classification
E1.1 Full written paper - refereed
Copyright notice
2008, IEEE
Title of proceedings
CVPR 2008 : Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition