Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

Falah Obaid*, Amin Babadi, Ahmad Yoosofan

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Deep learning is a new branch of machine learning, which is widely used by researchers in a lot of artificial intelligence applications, including signal processing and computer vision. The present research investigates the use of deep learning to solve the hand gesture recognition (HGR) problem and proposes two models using deep learning architecture. The first model comprises a convolutional neural network (CNN) and a recurrent neural network with a long short-term memory (RNN-LSTM). The accuracy of model achieves up to 82 % when fed by colour channel, and 89 % when fed by depth channel. The second model comprises two parallel convolutional neural networks, which are merged by a merge layer, and a recurrent neural network with a long short-term memory fed by RGB-D. The accuracy of the latest model achieves up to 93 %.

Original languageEnglish
Pages (from-to)57-61
Number of pages5
JournalApplied computer systems
Volume25
Issue number1
DOIs
Publication statusPublished - May 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Computer Vision (CV)
  • Convolutional Neural Network (CNN)
  • Deep Learning
  • Hand Gesture Recognition (HGR)
  • Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM)

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