sign-lang@LREC Anthology

The Importance of 3D Motion Trajectories for Computer-based Sign Recognition

Dilsizian, Mark | Tang, Zhiqiang | Metaxas, Dimitris | Huenerfauth, Matt | Neidle, Carol

Proceedings of the LREC2016 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining
Portorož, Slovenia
28 May 2016
European Language Resources Association (ELRA)
CC BY-NC 4.0
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American Sign Language


Computer-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.

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  author    = {Dilsizian, Mark and Tang, Zhiqiang and Metaxas, Dimitris and Huenerfauth, Matt and Neidle, Carol},
  title     = {The Importance of {3D} Motion Trajectories for Computer-based Sign Recognition},
  pages     = {53--58},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2016} 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining},
  maintitle = {10th International Conference on Language Resources and Evaluation ({LREC} 2016)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Portoro{\v z}, Slovenia},
  day       = {28},
  month     = may,
  year      = {2016},
  language  = {english},
  url       = {}
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