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

Independent Sign Language Recognition with 3D Body, Hands, and Face Reconstruction