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dc.contributor.authorDilsizian, Marken_US
dc.contributor.authorTang, Zhiqiangen_US
dc.contributor.authorMetaxas, Dimitrisen_US
dc.contributor.authorHuenerfauth, Matten_US
dc.contributor.authorNeidle, Carolen_US
dc.coverage.spatialPortorož, Sloveniaen_US
dc.date.accessioned2018-03-15T00:17:34Z
dc.date.available2018-03-15T00:17:34Z
dc.date.issued2016
dc.identifierhttp://www.lrec-conf.org/proceedings/lrec2016/workshops/LREC2016Workshop-SignLanguage_Proceedings.pdf
dc.identifier.citationMark Dilsizian, Zhiqiang Tang, Dimitris Metaxas, Matt Huenerfauth, Carol Neidle. "The Importance of 3D Motion Trajectories for Computer-based Sign Recognition." Proceedings of the 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining, Language Resources and Evaluation Conference 2016. 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining, Language Resources and Evaluation Conference 2016. Portorož, Slovenia, 2016-05-28 - 2016-05-28
dc.identifier.urihttps://hdl.handle.net/2144/27494
dc.description.abstractComputer-based sign language recognition from video is a challenging problem because of the spatiotemporal complexities inherent in sign production and the variations within and across signers. However, linguistic information can help constrain sign recognition to make it a more feasible classification problem. We have previously explored recognition of linguistically significant 3D hand configurations, as start and end handshapes represent one major component of signs; others include hand orientation, place of articulation in space, and movement. Thus, although recognition of handshapes (on one or both hands) at the start and end of a sign is essential for sign identification, it is not sufficient. Analysis of hand and arm movement trajectories can provide additional information critical for sign identification. In order to test the discriminative potential of the hand motion analysis, we performed sign recognition based exclusively on hand trajectories while holding the handshape constant. To facilitate this evaluation, we captured a collection of videos involving signs with a constant handshape produced by multiple subjects; and we automatically annotated the 3D motion trajectories. 3D hand locations are normalized in accordance with invariant properties of ASL movements. We trained time-series learning-based models for different signs of constant handshape in our dataset using the normalized 3D motion trajectories. Results show significant computer-based sign recognition accuracy across subjects and across a diverse set of signs. Our framework demonstrates the discriminative power and importance of 3D hand motion trajectories for sign recognition, given known handshapes.
dc.format.extent53 - 58 (6)en_US
dc.languageEnglishen_US
dc.publisherEuropean Language Resources Association (ELRA)en_US
dc.relation.ispartofProceedings of the 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining, Language Resources and Evaluation Conference 2016en_US
dc.rightsThis open access article is available with a Attribution-NonCommercial-NoDerivatives 4.0 International license. Copyright 2016 by the European Language Resources Associationen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectHand tracking
dc.subjectAmerican Sign Language
dc.subjectSign recognition
dc.subjectSign motion trajectory estimation
dc.titleThe importance of 3D motion trajectories for computer-based sign recognitionen_US
dc.typeConference materials
pubs.elements-sourcemanual-entryen_US
pubs.notesISBN 978-2-9517408-9-1 Copyright by the European Language Resources Associationen_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Romance Studiesen_US
pubs.publication-statusPublished onlineen_US
dc.date.online2016
dc.date.online2016


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This open access article is available with a Attribution-NonCommercial-NoDerivatives 4.0 International license. Copyright 2016 by the European Language Resources Association
Except where otherwise noted, this item's license is described as This open access article is available with a Attribution-NonCommercial-NoDerivatives 4.0 International license. Copyright 2016 by the European Language Resources Association