3D Hand Gesture Recognition Using a Depth and Skeletal Dataset

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dc.contributor.authorSmedt, Quentin Deen_US
dc.contributor.authorWannous, Hazemen_US
dc.contributor.authorVandeborre, Jean-Philippeen_US
dc.contributor.authorGuerry, J.en_US
dc.contributor.authorSaux, B. Leen_US
dc.contributor.authorFilliat, D.en_US
dc.contributor.editorIoannis Pratikakis and Florent Dupont and Maks Ovsjanikoven_US
dc.date.accessioned2017-04-22T17:17:41Z
dc.date.available2017-04-22T17:17:41Z
dc.date.issued2017
dc.description.abstractHand 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.en_US
dc.description.sectionheadersSHREC Session I
dc.description.seriesinformationEurographics Workshop on 3D Object Retrieval
dc.identifier.doi10.2312/3dor.20171049
dc.identifier.isbn978-3-03868-030-7
dc.identifier.issn1997-0471
dc.identifier.pages33-38
dc.identifier.urihttps://doi.org/10.2312/3dor.20171049
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3dor20171049
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.5 [Computer Graphics]
dc.subjectComputational Geometry and Object Modeling
dc.subjectI.2.10 [Artificial Intelligence]
dc.subjectVision and Scene Understanding
dc.subjectShape
dc.title3D Hand Gesture Recognition Using a Depth and Skeletal Dataseten_US
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