Multimodal Learning for Sign Language Recognition

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Ferreira, PM (Autor) Outra; Jaime S Cardoso (Autor) FEUP; Rebelo, A (Autor) Outra

Ata de Conferência Internacional

Título: PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2017)

Páginas: 313-321

8th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)

Univ Algarve, Faro, PORTUGAL, JUN 20-23, 2017

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 4 Citações

Outras Informações

ID Authenticus: P-00N-B26

DOI: 10.1007/978-3-319-58838-4_35

Abstract (EN): Sign Language Recognition (SLR) has becoming one of the most important research areas in the field of human computer interaction. SLR systems are meant to automatically translate sign language into text or speech, in order to reduce the communicational gap between deaf and hearing people. The aim of this paper is to exploit multimodal learning techniques for an accurate SLR, making use of data provided by Kinect and Leap Motion. In this regard, single-modality approaches as well as different multimodal methods, mainly based on convolutional neural networks, are proposed. Experimental results demonstrate that multimodal learning yields an overall improvement in the sign recognition performance.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9