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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728183671
DOI
EstadoPublicada - 21 oct. 2020
Publicado de forma externa
Evento2020 IEEE Engineering International Research Conference, EIRCON 2020 - Lima, Perú
Duración: 21 oct. 202023 oct. 2020

Serie de la publicación

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

Conferencia

Conferencia2020 IEEE Engineering International Research Conference, EIRCON 2020
País/TerritorioPerú
CiudadLima
Período21/10/2023/10/20

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