  Dynamic hand gesture recognition has attracted increasing interests because
of its importance for human computer interaction. In this paper, we propose a
new motion feature augmented recurrent neural network for skeleton-based
dynamic hand gesture recognition. Finger motion features are extracted to
describe finger movements and global motion features are utilized to represent
the global movement of hand skeleton. These motion features are then fed into a
bidirectional recurrent neural network (RNN) along with the skeleton sequence,
which can augment the motion features for RNN and improve the classification
performance. Experiments demonstrate that our proposed method is effective and
outperforms start-of-the-art methods.
