  The dynamics of human skeletons have significant information for the task of
action recognition. The similarity between trajectories of corresponding joints
is an indicating feature of the same action, while this similarity may subject
to some distortions that can be modeled as the combination of spatial and
temporal affine transformations. In this work, we propose a novel feature
called spatio-temporal dual affine differential invariant (STDADI).
Furthermore, in order to improve the generalization ability of neural networks,
a channel augmentation method is proposed. On the large scale action
recognition dataset NTU-RGB+D, and its extended version NTU-RGB+D 120, it
achieves remarkable improvements over previous state-of-the-art methods.
