Data collection of 3D spatial features of gestures from static peruvian sign language alphabet for sign language recognition
dc.contributor.author | Nurena-Jara, Roberto | |
dc.contributor.author | Ramos-Carrion, Cristopher | |
dc.contributor.author | Shiguihara-Juarez, Pedro | |
dc.date.accessioned | 2021-07-06T17:43:28Z | |
dc.date.available | 2021-07-06T17:43:28Z | |
dc.date.issued | 2020-10-21 | |
dc.identifier.doi | 10.1109/EIRCON51178.2020.9254019 | |
dc.identifier.uri | http://hdl.handle.net/10757/656634 | |
dc.description | El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. | en_US |
dc.description.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. | en_US |
dc.format | application/html | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.url | https://ieeexplore.ieee.org/document/9254019 | en_US |
dc.rights | info:eu-repo/semantics/embargoedAccess | en_US |
dc.subject | gesture recognition | en_US |
dc.subject | Peruvian sign language | en_US |
dc.subject | sign language recognition | en_US |
dc.title | Data collection of 3D spatial features of gestures from static peruvian sign language alphabet for sign language recognition | en_US |
dc.type | info:eu-repo/semantics/article | en_US |
dc.identifier.journal | Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020 | en_US |
dc.description.peerreview | Revisión por pares | es_PE |
dc.identifier.eid | 2-s2.0-85097807930 | |
dc.identifier.scopusid | SCOPUS_ID:85097807930 | |
dc.source.journaltitle | Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020 |