Journal
NEUROCOMPUTING
Volume 331, Issue -, Pages 424-433Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2018.11.038
Keywords
Hand gesture recognition; Key frames extraction; Feature fusion; Fast; Robust
Categories
Funding
- National Natural Science Foundation of China (NSFC) [U1613209]
- Shenzhen Key Laboratory for Intelligent Multimedia and Virtual Reality [ZDSYS201703031405467]
- Scientific Research Project of Shenzhen City [JCYJ20170306164738129]
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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 North-western University, Cambridge, HandGesture and Action3D hand gesture datasets. Our code and datasets will release at https://github.com/Ha0Tang/HandGestureRecognition. (C) 2018 Elsevier B.V. All rights reserved.
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