@InProceedings{BogdanRaducanu2010,
author="Bogdan Raducanu
and Fadi Dornaika",
title="Dynamic Facial Expression Recognition Using Laplacian Eigenmaps-Based Manifold Learning",
booktitle="IEEE International Conference on Robotics and Automation",
year="2010",
pages="156--161",
abstract="In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are: (i) a view- and texture independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker; (ii) the complexity of the non-linear facial expression space is modelled through a manifold, whose structure is learned using Laplacian Eigenmaps. The projected facial expressions are afterwards recognized based on Nearest Neighbor classifier; (iii) with the proposed approach, we developed an application for an AIBO robot, in which it mirrors the perceived facial expression.",
optnote="OR; MV",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1310), last updated on Wed, 28 Jul 2021 10:06:22 +0200",
isbn="978-1-4244-5038-1",
issn="1050-4729",
doi="10.1109/ROBOT.2010.5509290"
}