СROSS PLATFORM TOOLS FOR MODELING AND RECOGNITION OF THE FINGERSPELLING ALPHABET OF GESTURE LANGUAGE
Serhii Kondratiuk
Taras Shevchenko National University of Kyiv (Ukraine)
http://orcid.org/0000-0002-5048-2576
Iurii Krak
krak@univ.kiev.ua1Taras Shevchenko National University of Kyiv, 2Glushkov Institute of Cybernetics of NAS of Ukraine (Ukraine)
http://orcid.org/0000-0002-8043-0785
Waldemar Wójcik
Lublin Univeristy of Technology, Institute of Electronics and Computer Science (Poland)
http://orcid.org/0000-0002-0843-8053
Abstract
A solution for the problems of the finger spelling alphabet of gesture language modelling and recognition based on cross-platform technologies is proposed. Modelling and recognition performance can be flexible and adjusted, based on the hardware it operates or based on the availability of an internet connection. The proposed approach tunes the complexity of the 3D hand model based on the CPU type, amount of available memory and internet connection speed. Sign recognition is also performed using cross-platform technologies and the tradeoff in model size and performance can be adjusted. the methods of convolutional neural networks are used as tools for gestures of alphabet recognition. For the gesture recognition experiment, a dataset of 50,000 images was collected, with 50 different hands recorded, with almost 1,000 images per each person. The experimental researches demonstrated the effectiveness of proposed approaches.
Keywords:
cross platform, sign language, fingerspelling alphabet, 3D modeling, Convolutional Neural NetworksReferences
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Authors
Serhii KondratiukTaras Shevchenko National University of Kyiv Ukraine
http://orcid.org/0000-0002-5048-2576
Authors
Iurii Krakkrak@univ.kiev.ua
1Taras Shevchenko National University of Kyiv, 2Glushkov Institute of Cybernetics of NAS of Ukraine Ukraine
http://orcid.org/0000-0002-8043-0785
Authors
Waldemar WójcikLublin Univeristy of Technology, Institute of Electronics and Computer Science Poland
http://orcid.org/0000-0002-0843-8053
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