@InProceedings{BogdanRaducanu2012,
author="Bogdan Raducanu
and Fadi Dornaika",
title="Appearance-based Face Recognition Using A Supervised Manifold Learning Framework",
booktitle="IEEE Workshop on the Applications of Computer Vision",
year="2012",
publisher="IEEE Xplore",
pages="465--470",
abstract="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{\textquoteright}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.",
optnote="OR;MV",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1890), last updated on Mon, 15 May 2017 10:29:31 +0200",
isbn="978-1-4673-0233-3",
issn="1550-5790",
doi="10.1109/WACV.2012.6163045",
file=":http://refbase.cvc.uab.es/files/RaD2012d.pdf:PDF"
}