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Classification of Mouth Gestures in German Sign Language using 3D Convolutional Neural Networks
Publikationstyp
Conference Paper
Date Issued
2019-07
Sprache
English
Author(s)
Institut
TORE-URI
Volume
2015
Issue
CP761
Start Page
52
End Page
57
Citation
10th International Conference on Pattern Recognition Systems CP761: 52-57 (2019)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
IEEE
Automatic recognition of sign language gestures is becoming necessary with an increased interest into human-computer interaction in sign language as well as automatic translation from sign language. Most of the research on sign language recognition focuses on hand gesture recognition. However, there are also non-manual signals in sign language. Mouth gestures represent mouth shapes that add information to the hand gestures not related to spoken language visemes. For German Sign Language, mouth gesture recognition would be an important addition to manual gesture recognition. This research work evaluates the method 3D convolutional neural networks for recognising mouth gestures in German Sign Language. For the recognition of certain mouth gestures, temporal information is mandatory and the extraction of both spatial and temporal features by 3D convolutional networks makes the classification of all gestures easier. Our research work compares how different initialisations affect learning and classification by the network. We achieve an accuracy of around 68% on testing 10 classes of mouth gestures in German Sign Language. © 2019 IET Conference Publications. All rights reserved.
DDC Class
600: Technology