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Gesture recognition is a hot topic in computer vision and patternrecognition, which plays a vitally important role in natural human-computerinterface. Although great progress has been made recently, fast and robust handgesture recognition remains an open problem, since the existing methods havenot well balanced the performance and the efficiency simultaneously. To bridgeit, this work combines image entropy and density clustering to exploit the keyframes from hand gesture video for further feature extraction, which canimprove the efficiency of recognition. Moreover, a feature fusion strategy isalso proposed to further improve feature representation, which elevates theperformance 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 competitiveresults on Northwestern University, Cambridge, HandGesture and Action3D handgesture datasets. Our code and datasets will release athttps://github.com/Ha0Tang/HandGestureRecognition.

Fast and Robust Dynamic Hand Gesture Recognition via Key Frames Extraction and Feature Fusion