PT Unknown
AU Bogdan Raducanu
   Fadi Dornaika
TI Appearance-based Face Recognition Using A Supervised Manifold Learning Framework
BT IEEE Workshop on the Applications of Computer Vision
PY 2012
BP 465
EP 470
DI 10.1109/WACV.2012.6163045
AB Many natural image sets, depicting objects whose appearance is changing due to motion, pose or light variations, can be considered samples of a low-dimension nonlinear manifold embedded in the high-dimensional observation space (the space of all possible images). The main contribution of our work is represented by a Supervised Laplacian Eigemaps (S-LE) algorithm, which exploits the class label information for mapping the original data in the embedded space. Our proposed approach benefits from two important properties: i) it is discriminative, and ii) it adaptively selects the neighbors of a sample without using any predefined neighborhood size. Experiments were conducted on four face databases and the results demonstrate that the proposed algorithm significantly outperforms many linear and non-linear embedding techniques. Although we've focused on the face recognition problem, the proposed approach could also be extended to other category of objects characterized by large variance in their appearance.
ER