3D Hand Gesture Recognition Using a Depth and Skeletal Dataset
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2017Pay-Per-View via TIB Hannover:
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Hand gesture recognition is recently becoming one of the most attractive field of research in pattern recognition. The objective of this track is to evaluate the performance of recent recognition approaches using a challenging hand gesture dataset containing 14 gestures, performed by 28 participants executing the same gesture with two different numbers of fingers. Two research groups have participated to this track, the accuracy of their recognition algorithms have been evaluated and compared to three other state-of-the-art approaches.
BibTeX
@inproceedings {10.2312:3dor.20171049,
booktitle = {Eurographics Workshop on 3D Object Retrieval},
editor = {Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov},
title = {{3D Hand Gesture Recognition Using a Depth and Skeletal Dataset}},
author = {Smedt, Quentin De and Wannous, Hazem and Vandeborre, Jean-Philippe and Guerry, J. and Saux, B. Le and Filliat, D.},
year = {2017},
publisher = {The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-030-7},
DOI = {10.2312/3dor.20171049}
}
booktitle = {Eurographics Workshop on 3D Object Retrieval},
editor = {Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov},
title = {{3D Hand Gesture Recognition Using a Depth and Skeletal Dataset}},
author = {Smedt, Quentin De and Wannous, Hazem and Vandeborre, Jean-Philippe and Guerry, J. and Saux, B. Le and Filliat, D.},
year = {2017},
publisher = {The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-03868-030-7},
DOI = {10.2312/3dor.20171049}
}