Paper Info
Title
Gesture Recognition Based On Multi-Modal Feature Weight
Abstract
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.
Year
DOI
Venue
2021
10.1002/cpe.5991
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
Keywords
DocType
Volume
gesture recognition, RGB-D, multi-modal fusion, weight adaptation
Journal
33
Issue
ISSN
Citations 
5
1532-0626
1
PageRank 
References 
Authors
0.35
0
8
Name
Order
Citations
PageRank
Haojie Duan110.35
Ying Sun229140.03
Wentao Cheng381.11
Du Jiang49714.40
Juntong Yun544.44
Ying Liu632.07
Yibo Liu713.39
Dalin Zhou8168.09