Data collection of 3D spatial features of gestures from static peruvian sign language alphabet for sign language recognition

Roberto Nurena-Jara, Cristopher Ramos-Carrion, Pedro Shiguihara-Juarez

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

1 Scopus citations

Abstract

Peruvian Sign Language Recognition (PSL) is approached as a classification problem. Previous work has employed 2D features from the position of hands to tackle this problem. In this paper, we propose a method to construct a dataset consisting of 3D spatial positions of static gestures from the PSL alphabet, using the HTC Vive device and a well-known technique to extract 21 keypoints from the hand to obtain a feature vector. A dataset of 35, 400 instances of gestures for PSL was constructed and a novel way to extract data was stated. To validate the appropriateness of this dataset, a comparison of four baselines classifiers in the Peruvian Sign Language Recognition (PSLR) task was stated, achieving 99.32% in the average in terms of F1 measure in the best case.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728183671
DOIs
StatePublished - 21 Oct 2020
Externally publishedYes
Event2020 IEEE Engineering International Research Conference, EIRCON 2020 - Lima, Peru
Duration: 21 Oct 202023 Oct 2020

Publication series

NameProceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020

Conference

Conference2020 IEEE Engineering International Research Conference, EIRCON 2020
Country/TerritoryPeru
CityLima
Period21/10/2023/10/20

Keywords

  • gesture recognition
  • Peruvian sign language
  • sign language recognition

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