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Gesture recognition based on multi‐modal feature weight
Author(s) -
Duan Haojie,
Sun Ying,
Cheng Wentao,
Jiang Du,
Yun Juntong,
Liu Ying,
Liu Yibo,
Zhou Dalin
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5991
Subject(s) - rgb color model , gesture , gesture recognition , computer science , robustness (evolution) , artificial intelligence , computer vision , modal , fuse (electrical) , pattern recognition (psychology) , feature (linguistics) , speech recognition , engineering , polymer chemistry , biochemistry , chemistry , linguistics , philosophy , electrical engineering , gene
Summary With the continuous development of sensor technology, the acquisition cost of RGB‐D images is getting lower and lower, and gesture recognition based on depth images and Red‐Green‐Blue (RGB) images has gradually become a research direction in the field of pattern recognition. However, most of the current processing methods for RGB‐D gesture images are relatively simple, ignoring the relationship and influence between its two modes, and unable to make full use of the correlation factors between different modes. In view of the above problems, this paper optimizes the effect of RGB‐D information processing by considering the independent features and related features of multi‐modal data to construct a weight adaptive algorithm to fuse different features. Simulation experiments show that the method proposed in this paper is better than the traditional RGB‐D gesture image processing method and the gesture recognition rate is higher. Comparing the current more advanced gesture recognition methods, the method proposed in this paper also achieves higher recognition accuracy, which verifies the feasibility and robustness of this method.