  Over the years, hand gesture recognition has been mostly addressed
considering hand trajectories in isolation. However, in most sign languages,
hand gestures are defined on a particular context (body region). We propose a
pipeline to perform sign language recognition which models hand movements in
the context of other parts of the body captured in the 3D space using the MS
Kinect sensor. In addition, we perform sign recognition based on the different
hand postures that occur during a sign. Our experiments show that considering
different body parts brings improved performance when compared to other methods
which only consider global hand trajectories. Finally, we demonstrate that the
combination of hand postures features with hand gestures features helps to
improve the prediction of a given sign.
