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Authors: Lieven Billiet 1 ; Jose Oramas 1 ; McElory Hoffmann 2 ; Wannes Meert 1 and Laura Antanas 1

Affiliations: 1 Katholieke Universiteit Leuven, Belgium ; 2 Katholieke Universiteit Leuven and Stellenbosch University, Belgium

Keyword(s): Hand Posture Recognition, 3D Data, Model-based Recognition, Rule-based Model.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Pattern Recognition ; Software Engineering ; Video Analysis

Abstract: Gesture recognition systems exhibit failures when faced with large hand posture vocabularies or relatively new hand poses. One main reason is that 2D and 3D appearance-based approaches require significant amounts of training data. We address this problem by introducing a new 2D model-based approach to recognize hand postures. The model captures a high-level rule-based representation of the hand expressed in terms of finger poses and their qualitative configuration. The available 3D information is used to first segment the hand. We evaluate our approach on a Kinect dataset and report superior results while using less training data when comparing to state-of-the-art 3D SURF descriptor.

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Paper citation in several formats:
Billiet, L.; Oramas, J.; Hoffmann, M.; Meert, W. and Antanas, L. (2013). Rule-based Hand Posture Recognition using Qualitative Finger Configurations Acquired with the Kinect. In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-8565-41-9; ISSN 2184-4313, SciTePress, pages 539-542. DOI: 10.5220/0004230805390542

@conference{icpram13,
author={Lieven Billiet. and Jose Oramas. and McElory Hoffmann. and Wannes Meert. and Laura Antanas.},
title={Rule-based Hand Posture Recognition using Qualitative Finger Configurations Acquired with the Kinect},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2013},
pages={539-542},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004230805390542},
isbn={978-989-8565-41-9},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Rule-based Hand Posture Recognition using Qualitative Finger Configurations Acquired with the Kinect
SN - 978-989-8565-41-9
IS - 2184-4313
AU - Billiet, L.
AU - Oramas, J.
AU - Hoffmann, M.
AU - Meert, W.
AU - Antanas, L.
PY - 2013
SP - 539
EP - 542
DO - 10.5220/0004230805390542
PB - SciTePress

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