  Existing action recognition methods mainly focus on joint and bone
information in human body skeleton data due to its robustness to complex
backgrounds and dynamic characteristics of the environments. In this paper, we
combine body skeleton data with spatial and motion features from face and two
hands, and present "Deep Action Stamps (DeepActs)", a novel data representation
to encode actions from video sequences. We also present "DeepActsNet", a deep
learning based ensemble model which learns convolutional and structural
features from Deep Action Stamps for highly accurate action recognition.
Experiments on three challenging action recognition datasets (NTU60, NTU120,
and SYSU) show that the proposed model trained using Deep Action Stamps produce
considerable improvements in the action recognition accuracy with less
computational cost compared to the state-of-the-art methods.
