SHREC'17 Track: 3D Hand Gesture Recognition Using a Depth and Skeletal Dataset - Archive ouverte HAL Access content directly
Conference Papers Year : 2017

SHREC'17 Track: 3D Hand Gesture Recognition Using a Depth and Skeletal Dataset

Abstract

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.
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Dates and versions

hal-01563505 , version 1 (17-07-2017)

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Quentin de Smedt, Hazem Wannous, Jean-Philippe Vandeborre, Joris Guerry, Bertrand Le Saux, et al.. SHREC'17 Track: 3D Hand Gesture Recognition Using a Depth and Skeletal Dataset. 3DOR - 10th Eurographics Workshop on 3D Object Retrieval, Apr 2017, Lyon, France. pp.1-6, ⟨10.2312/3dor.20171049⟩. ⟨hal-01563505⟩
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