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

Nurena-Jara R.; Ramos-Carrion C.; Shiguihara-Juarez P. · Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020 (2020) · DOI: 10.1109/eircon51178.2020.9254019

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.

Instituciones: Universidad Peruana de Ciencias Aplicadas