  It is known that the kinematics of the human body skeleton reveals valuable
information in action recognition. Recently, modeling skeletons as
spatio-temporal graphs with Graph Convolutional Networks (GCNs) has been
reported to solidly advance the state-of-the-art performance. However,
GCN-based approaches exclusively learn from raw skeleton data, and are expected
to extract the inherent structural information on their own. This paper
describes REGINA, introducing a novel way to REasoning Graph convolutional
networks IN Human Action recognition. The rationale is to provide to the GCNs
additional knowledge about the skeleton data, obtained by handcrafted features,
in order to facilitate the learning process, while guaranteeing that it remains
fully trainable in an end-to-end manner. The challenge is to capture
complementary information over the dynamics between consecutive frames, which
is the key information extracted by state-of-the-art GCN techniques. Moreover,
the proposed strategy can be easily integrated in the existing GCN-based
methods, which we also regard positively. Our experiments were carried out in
well known action recognition datasets and enabled to conclude that REGINA
contributes for solid improvements in performance when incorporated to other
GCN-based approaches, without any other adjustment regarding the original
method. For reproducibility, the REGINA code and all the experiments carried
out will be publicly available at https://github.com/DegardinBruno.
