Comparison of Recognition Accuracy on Dynamic Hand Gesture Using Feature Selection

Adri Gabriel Sooai, Patrisius Batarius, Yovinia Carmeneja Hoar Siki, Paskalis Andrianus Nani, Natalia Magdalena Rafu Mamulak, Emerensiana Ngaga, Ulla Delfana Rosiani, Surya Sumpeno, Mauridhi Hery Purnomo, Sisilia Daeng Bakka Mau

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

7 Citations (Scopus)

Abstract

Dynamic Hand Gesture Recognition is carried out in various studies to read patterns. Various sensors can be used to capture dynamic hand movement patterns. The results of the initial reading are usually in the form of raw data that must pass initial processing. Advanced processing is carried out to obtain features that will be trained using various classifiers. The recognition process without feature selection activities will reduce the accuracy of pattern recognition during the classification process. Seeing the many shortcomings in the implementation of the initial data processing, this study will present some initial processing examples to produce features that are relatively good for the data training process. The method used is the Gaussian mixture model and the selection of predictors for the classification process. The sensor for recording dynamic hand movements used in this study is Leap-motion. There are three dynamic hand gestures were used in this study. The data used were 4609 coordinates spread in 30 features. The classifiers used are k-NN with Euclidean distance metric without feature selection process compare to the process with feature selection. The results obtained from this study are the availability of examples of feature selection models in the form of Gaussian mixtures and some accuracy of the results of processing comparison. The lowest prediction, without and with feature prediction, has a slightly range from 99.7% to 100%.

Original languageEnglish
Title of host publication2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages270-274
Number of pages5
ISBN (Electronic)9781538675090
DOIs
Publication statusPublished - 2 Jul 2018
Event2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Surabaya, Indonesia
Duration: 26 Nov 201827 Nov 2018

Publication series

Name2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding

Conference

Conference2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018
Country/TerritoryIndonesia
CitySurabaya
Period26/11/1827/11/18

Keywords

  • Distance Metric
  • Dynamic Hand Gesture
  • Feature Selection
  • Gaussian Mixture Model
  • Recognition Accuracy

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