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Sign Language Numeral Gestures Recognition Using Convolutional Neural Network

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43952760" target="_blank" >RIV/49777513:23520/18:43952760 - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/chapter/10.1007%2F978-3-319-99582-3_8" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-99582-3_8</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-99582-3_8" target="_blank" >10.1007/978-3-319-99582-3_8</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Sign Language Numeral Gestures Recognition Using Convolutional Neural Network

  • Original language description

    This paper presents usage of convolutional neural network for classification of sign language numeral gestures. For requirements of this research, we created a new dataset of these gestures. The dataset was recorded via Kinect v2 device and it consists of recordings of 18 different people. Only depth data-stream was used in our research. For a classification task, there was utilized classic VGG16 architecture and its results were compared with chosen baseline method and other tested architectures. Our experiment on classification showed the great potential of neural networks for this task. We reached recognition accuracy 86.45%, which is by more than 34% better result than chosen baseline method.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20205 - Automation and control systems

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Interactive Collaborative Robotics Third International Conference, ICR 2018 Leipzig, Germany, September 18–22, 2018 Proceedings

  • ISBN

    978-3-319-99581-6

  • ISSN

    0302-9743

  • e-ISSN

    1611-3349

  • Number of pages

    8

  • Pages from-to

    70-77

  • Publisher name

    Springer Nature Switzerland AG

  • Place of publication

    Cham

  • Event location

    Leipzig, Germany

  • Event date

    Sep 18, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article