@InProceedings{BogdanRaducanu2012,
author="Bogdan Raducanu
and Fadi Dornaika",
title="Pose-Invariant Face Recognition in Videos for Human-Machine Interaction",
booktitle="12th European Conference on Computer Vision",
year="2012",
publisher="Springer Berlin Heidelberg",
volume="7584",
pages="566.575",
abstract="Human-machine interaction is a hot topic nowadays in the communities of computer vision and robotics. In this context, face recognition algorithms (used as primary cue for a person{\textquoteright}s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, pose, and occlusions. In this paper, we propose a novel approach for robust pose-invariant face recognition for human-robot interaction based on the real-time fitting of a 3D deformable model to input images taken from video sequences. More concrete, our approach generates a rectified face image irrespective with the actual head-pose orientation. Experimental results performed on Honda video database, using several manifold learning techniques, show a distinct advantage of the proposed method over the standard 2D appearance-based snapshot approach.",
optnote="OR;MV",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2182), last updated on Tue, 18 Oct 2016 12:03:25 +0200",
isbn="978-3-642-33867-0",
issn="0302-9743",
doi="10.1007/978-3-642-33868-7_56",
file=":http://refbase.cvc.uab.es/files/RaD2012e.pdf:PDF"
}