Feature Extraction with Video Summarization of Dynamic Gestures for Peruvian Sign Language Recognition

Andre Neyra-Gutierrez, Pedro Shiguihara-Juarez

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

2 Citas (Scopus)

Resumen

In peruvian sign language (PSL), recognition of static gestures has been proposed earlier. However, to state a conversation using sign language, it is also necessary to employ dynamic gestures. We propose a method to extract a feature vector for dynamic gestures of PSL. We collect a dataset with 288 video sequences of words related to dynamic gestures and we state a workflow to process the keypoints of the hands, obtaining a feature vector for each video sequence with the support of a video summarization technique. We employ 9 neural networks to test the method, achieving an average accuracy ranging from 80% and 90%, using 10 fold cross-validation.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728193779
DOI
EstadoPublicada - set. 2020
Publicado de forma externa
Evento27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 - Virtual, Lima, Perú
Duración: 3 set. 20205 set. 2020

Serie de la publicación

NombreProceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020

Conferencia

Conferencia27th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020
País/TerritorioPerú
CiudadVirtual, Lima
Período3/09/205/09/20

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