Research Area: Machine Learning
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.
Keywords:
Hand gesture recognition
Key frames extraction
Feature fusion
Fast
Robust
Author(s) Name: Hao Tang, Hong Liu, Wei Xiao, Nicu Sebe
Journal name: Neurocomputing
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.neucom.2018.11.038
Volume Information: Volume 331, 28 February 2019, Pages 424-433
