  Pose based hand gesture recognition has been widely studied in the recent
years. Compared with full body action recognition, hand gesture involves joints
that are more spatially closely distributed with stronger collaboration. This
nature requires a different approach from action recognition to capturing the
complex spatial features. Many gesture categories, such as "Grab" and "Pinch",
have very similar motion or temporal patterns posing a challenge on temporal
processing. To address these challenges, this paper proposes a two-stream
neural network with one stream being a self-attention based graph convolutional
network (SAGCN) extracting the short-term temporal information and hierarchical
spatial information, and the other being a residual-connection enhanced
bidirectional Independently Recurrent Neural Network (RBi-IndRNN) for
extracting long-term temporal information. The self-attention based graph
convolutional network has a dynamic self-attention mechanism to adaptively
exploit the relationships of all hand joints in addition to the fixed topology
and local feature extraction in the GCN. On the other hand, the
residual-connection enhanced Bi-IndRNN extends an IndRNN with the capability of
bidirectional processing for temporal modelling. The two streams are fused
together for recognition. The Dynamic Hand Gesture dataset and First-Person
Hand Action dataset are used to validate its effectiveness, and our method
achieves state-of-the-art performance.
