@InProceedings{CardenasCernChav:2019:DySiLa,
               author = "Cardenas, Edwin Jonathan Escobedo and Cerna, Lourdes Ramirez and 
                         Chavez, Guillermo Camara",
          affiliation = "{Federal University of Ouro Preto} and {National University of 
                         Ouro Preto} and {Federal University of Ouro Preto}",
                title = "Dynamic Sign Language Recognition Based on Convolutional Neural 
                         Networks and Texture Maps",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "CNN, sign language, texture maps.",
             abstract = "Sign language recognition (SLR) is a very challenging task due to 
                         the complexity of learning or developing descriptors to represent 
                         its primary parameters (location, movement, and hand 
                         configuration). In this paper, we propose a robust deep learning 
                         based method for sign language recognition. Our approach 
                         represents multimodal information (RGB-D) through texture maps to 
                         describe the hand location and movement. Moreover, we introduce an 
                         intuitive method to extract a representative frame that describes 
                         the hand shape. Next, we use this information as inputs to two 
                         three-stream and two-stream CNN models to learn robust features 
                         capable of recognizing a dynamic sign. We conduct our experiments 
                         on two sign language datasets, and the comparison with 
                         state-of-the-art SLR methods reveal the superiority of our 
                         approach which optimally combines texture maps and hand shape for 
                         SLR tasks.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00043",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00043",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U3ETBS",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U3ETBS",
           targetfile = "PID111.pdf",
        urlaccessdate = "2024, Apr. 27"
}