Recognition of Static Features in Sign Language Using Key-Points

Ioannis Koulierakis, Georgios Siolas, Eleni Efthimiou, Evita Fotinea, Andreas-Georgios Stafylopatis


Abstract
In this paper we report on a research effort focusing on recognition of static features of sign formation in single sign videos. Three sequential models have been developed for handshape, palm orientation and location of sign formation respectively, which make use of key-points extracted via OpenPose software. The models have been applied to a Danish and a Greek Sign Language dataset, providing results around 96%. Moreover, during the reported research, a method has been developed for identifying the time-frame of real signing in the video, which allows to ignore transition frames during sign recognition processing.
Anthology ID:
2020.signlang-1.20
Volume:
Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
LREC | SignLang | WS
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
123–126
Language:
English
URL:
https://aclanthology.org/2020.signlang-1.20
DOI:
Bibkey:
Cite (ACL):
Ioannis Koulierakis, Georgios Siolas, Eleni Efthimiou, Evita Fotinea, and Andreas-Georgios Stafylopatis. 2020. Recognition of Static Features in Sign Language Using Key-Points. In Proceedings of the LREC2020 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives, pages 123–126, Marseille, France. European Language Resources Association (ELRA).
Cite (Informal):
Recognition of Static Features in Sign Language Using Key-Points (Koulierakis et al., SignLang 2020)
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PDF:
https://aclanthology.org/2020.signlang-1.20.pdf