  In this paper, we propose a 3D Convolutional Neural Network (3DCNN) based
multi-stream framework to recognize American Sign Language (ASL) manual signs
(consisting of movements of the hands, as well as non-manual face movements in
some cases) in real-time from RGB-D videos, by fusing multimodality features
including hand gestures, facial expressions, and body poses from multi-channels
(RGB, depth, motion, and skeleton joints). To learn the overall temporal
dynamics in a video, a proxy video is generated by selecting a subset of frames
for each video which are then used to train the proposed 3DCNN model. We
collect a new ASL dataset, ASL-100-RGBD, which contains 42 RGB-D videos
captured by a Microsoft Kinect V2 camera, each of 100 ASL manual signs,
including RGB channel, depth maps, skeleton joints, face features, and HDface.
The dataset is fully annotated for each semantic region (i.e. the time duration
of each word that the human signer performs). Our proposed method achieves
92.88 accuracy for recognizing 100 ASL words in our newly collected
ASL-100-RGBD dataset. The effectiveness of our framework for recognizing hand
gestures from RGB-D videos is further demonstrated on the Chalearn IsoGD
dataset and achieves 76 accuracy which is 5.51 higher than the state-of-the-art
work in terms of average fusion by using only 5 channels instead of 12 channels
in the previous work.
