Gesture recognition based on depth information and convolutional neural network

Du Jiang, Gongfa Li, Guozhang Jiang, Disi Chen, Zhaojie Ju

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Vision-based gesture Recognition accords with natural communication habits of human and can carry out long-distance and non-contact interactions. So it has become a hot direction in human-computer interaction research whose Recognition effect largely depends on the performance of image preprocessing and Recognition algorithms. In this paper, a gesture Recognition method using color image and depth image combined is designed. For the influence of the angle on the same gesture, the skeleton algorithm is optimized based on the layer-by-layer stripping concept. The fast refinement algorithm improves the process of repeated scanning, extracts the key node information in the skeleton map of the hand, and establishes the spatial axis of the hand to determine the gesture direction. The gesture Recognition experiment was performed based on convolutional neural network. The results showed the Recognition accuracy rate was 96.01%, and the robustness and accuracy of the proposed Recognition method were verified.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PublisherIEEE
Pages4041-4046
Number of pages6
ISBN (Electronic)978-1-5386-6650-0
ISBN (Print)978-1-5386-6651-7
DOIs
Publication statusPublished - 17 Jan 2019
Event2018 IEEE International Conference on Systems, Man and Cybernetics - Miyazaki, Japan
Duration: 7 Oct 201810 Oct 2018
http://www.smc2018.org/

Publication series

NameIEEE SMC Proceedings Series
PublisherIEEE
ISSN (Print)1062-922X
ISSN (Electronic)2577-1655

Conference

Conference2018 IEEE International Conference on Systems, Man and Cybernetics
Abbreviated titleSMC 2018
Country/TerritoryJapan
CityMiyazaki
Period7/10/1810/10/18
Internet address

Keywords

  • Convolutional neural network
  • Depth information
  • Gesture Recognition
  • Hand skeleton extraction

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