  Gesture recognition is a hot topic in computer vision and pattern
recognition, which plays a vitally important role in natural human-computer
interface. Although great progress has been made recently, fast and robust hand
gesture recognition remains an open problem, since the existing methods have
not well balanced the performance and the efficiency simultaneously. To bridge
it, this work combines image entropy and density clustering to exploit the key
frames from hand gesture video for further feature extraction, which can
improve the efficiency of recognition. Moreover, a feature fusion strategy is
also proposed to further improve feature representation, which elevates the
performance of recognition. To validate our approach in a "wild" environment,
we also introduce two new datasets called HandGesture and Action3D datasets.
Experiments consistently demonstrate that our strategy achieves competitive
results on Northwestern University, Cambridge, HandGesture and Action3D hand
gesture datasets. Our code and datasets will release at
https://github.com/Ha0Tang/HandGestureRecognition.
