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Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition

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Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11731))

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Abstract

The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. We propose a new approach of Spatial-Temporal Graph Convolutional Network for sign language recognition based on the human skeletal movements. The method uses graphs to capture the dynamics of the signs in two dimensions, spatial and temporal, considering the complex aspects of the language. Additionally, we present a new dataset of human skeletons for sign language based on ASLLVD to contribute to future related studies.

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Notes

  1. 1.

    Dactylology - digital or manual alphabet, generally used by the deaf to introduce a word that does not yet have an equivalent sign [11].

  2. 2.

    Available at https://github.com/yysijie/st-gcn.

  3. 3.

    Available at http://csr.bu.edu/asl/asllvd/annotate/index.html.

  4. 4.

    Available at http://www.cin.ufpe.br/~cca5/asllvd-skeleton.

  5. 5.

    Available at http://www.cin.ufpe.br/~cca5/st-gcn-sl.

  6. 6.

    Available at http://www.cin.ufpe.br/~cca5/asllvd-skeleton-20.

References

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Authors and Affiliations

  1. Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, 50740-560, Brazil

    Cleison Correia de Amorim, David Macêdo & Cleber Zanchettin

Authors
  1. Cleison Correia de Amorim
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  2. David Macêdo
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  3. Cleber Zanchettin
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Corresponding author

Correspondence to Cleison Correia de Amorim .

Editor information

Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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de Amorim, C.C., Macêdo, D., Zanchettin, C. (2019). Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_59

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  • DOI: https://doi.org/10.1007/978-3-030-30493-5_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30492-8

  • Online ISBN: 978-3-030-30493-5

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