  Existing 3D skeleton-based action recognition approaches reach impressive
performance by encoding handcrafted action features to image format and
decoding by CNNs. However, such methods are limited in two ways: a) the
handcrafted action features are difficult to handle challenging actions, and b)
they generally require complex CNN models to improve action recognition
accuracy, which usually occur heavy computational burden. To overcome these
limitations, we introduce a novel AFE-CNN, which devotes to enhance the
features of 3D skeleton-based actions to adapt to challenging actions. We
propose feature enhance modules from key joint, bone vector, key frame and
temporal perspectives, thus the AFE-CNN is more robust to camera views and body
sizes variation, and significantly improve the recognition accuracy on
challenging actions. Moreover, our AFE-CNN adopts a light-weight CNN model to
decode images with action feature enhanced, which ensures a much lower
computational burden than the state-of-the-art methods. We evaluate the AFE-CNN
on three benchmark skeleton-based action datasets: NTU RGB+D, NTU RGB+D 120,
and UTKinect-Action3D, with extensive experimental results demonstrate our
outstanding performance of AFE-CNN.
