  Independent Sign Language Recognition is a complex visual recognition problem
that combines several challenging tasks of Computer Vision due to the necessity
to exploit and fuse information from hand gestures, body features and facial
expressions. While many state-of-the-art works have managed to deeply elaborate
on these features independently, to the best of our knowledge, no work has
adequately combined all three information channels to efficiently recognize
Sign Language. In this work, we employ SMPL-X, a contemporary parametric model
that enables joint extraction of 3D body shape, face and hands information from
a single image. We use this holistic 3D reconstruction for SLR, demonstrating
that it leads to higher accuracy than recognition from raw RGB images and their
optical flow fed into the state-of-the-art I3D-type network for 3D action
recognition and from 2D Openpose skeletons fed into a Recurrent Neural Network.
Finally, a set of experiments on the body, face and hand features showed that
neglecting any of these, significantly reduces the classification accuracy,
proving the importance of jointly modeling body shape, facial expression and
hand pose for Sign Language Recognition.
