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Hand Gesture Recognition Using Convolutional Neural Networks

  • Korea Advanced Institute of Science and Technology
  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper introduced a hand gesture recognition method based on convolutional neural networks (CNNs). The recognition scenario consisted in a three dimensional radar array to transmit and receive 24GHz continuous electromagnetic (EM) wave, and convert the scattered EM wave to the intermediate frequency (IF) signals. This paper used the the processed frequency spectrum as the input to the CNN. Then the CNN feature detection layer learned through data training, avoiding supervised feature extraction while learning implicitly from training data. It highlighted these features through convolution operating, pooling and a softmax function. Results showed that this system could achieve a high recognition accuracy rate higher than 96%.

Original languageEnglish
Title of host publication2018 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages147-148
Number of pages2
ISBN (Electronic)9781538671054
DOIs
StatePublished - 2 Jul 2018
Event2018 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2018 - Boston, United States
Duration: 8 Jul 201813 Jul 2018

Publication series

Name2018 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2018 - Proceedings

Conference

Conference2018 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2018
Country/TerritoryUnited States
CityBoston
Period8/07/1813/07/18

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