Sign language recognition model combining non-manual markers and handshapes

Abstract

People with disabilities have fewer opportunities. Technological developments should be used to help these people to have more opportunities. In this paper we present partial results of a research project which aims to help people with disabilities, specifically deaf and hard of hearing. We present a sign language recognition model. The model takes advantage of the natural user interfaces (NUI) and a classification algorithm (support vector machines). Moreover, we combine handshapes (signs) and non-manual markers (associated to emotions and face gestures) in the recognition process to enhance the sign language expressivity recognition. Additionally, non-manual markers representation is proposed. A model evaluation is also reported.