%0 Conference Proceedings
%T Appearance-based Face Recognition Using A Supervised Manifold Learning Framework
%A Bogdan Raducanu
%A Fadi Dornaika
%B IEEE Workshop on the Applications of Computer Vision
%D 2012
%I IEEE Xplore
%@ 1550-5790
%@ 978-1-4673-0233-3
%F Bogdan Raducanu2012
%O OR;MV
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1890), last updated on Mon, 15 May 2017 10:29:31 +0200
%X 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.
%U http://refbase.cvc.uab.es/files/RaD2012d.pdf
%U http://dx.doi.org/10.1109/WACV.2012.6163045
%P 465-470