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Method for Multimodal Recognition of One-Handed Sign Language Gestures Through 3D Convolution and LSTM Neural Networks

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Speech and Computer (SPECOM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11658))

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Abstract

The paper presents an approach to the multimodal recognition of dynamic and static gestures of Russian sign language through 3D convolutional and LSTM neural networks. A set of data in color format and a depth map, consisting of 48 one-handed gestures of Russian sign language, is presented as well. The set of data was obtained with the use of the Kinect sensor v2 and contains records of 13 different native signers of Russian sign language. The obtained results are compared with these of other methods. The experiment on classification showed a great potential of neural networks in solving this problem. Achieved recognition accuracy was of 73.25%, and, compared to other approaches to the problem, this turns out to be the best result.

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Acknowledgments

This research is financially supported by the Ministry of Science and Higher Education of the Russian Federation, agreement No. 14.616.21.0095 (reference RFMEFI61618X0095).

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Authors and Affiliations

  1. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia

    Ildar Kagirov, Dmitry Ryumin & Alexandr Axyonov

Authors
  1. Ildar Kagirov
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  2. Dmitry Ryumin
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  3. Alexandr Axyonov
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Corresponding author

Correspondence to Ildar Kagirov .

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Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Albert Ali Salah

  2. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  3. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Kagirov, I., Ryumin, D., Axyonov, A. (2019). Method for Multimodal Recognition of One-Handed Sign Language Gestures Through 3D Convolution and LSTM Neural Networks. In: Salah, A., Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2019. Lecture Notes in Computer Science(), vol 11658. Springer, Cham. https://doi.org/10.1007/978-3-030-26061-3_20

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  • DOI: https://doi.org/10.1007/978-3-030-26061-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26060-6

  • Online ISBN: 978-3-030-26061-3

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