Gesture recognition based on sparse representation

被引：0
|
作者：
Miao W. [1 ]
Li G. [1 ]
Sun Y. [1 ]
Jiang G. [3 ]
Kong J. [1 ]
Liu H. [2 ]
机构：
[1] College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan
[2] State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai
[3] Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth
来源：
International Journal of Wireless and Mobile Computing | 2016年 / 11卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Gesture recognition; HOG feature; Hu invariant moments; human-computer interaction; Sparse representation;
D O I：
10.1504/IJWMC.2016.082289
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the robustness of gesture recognition is difficult to guarantee, this paper presents a method based on multi-features and sparse representation. Hu invariant moments and HOG features of training samples are extracted in training phase. The K-SVD algorithm is used to train the initial value of dictionary formed by two features so as to obtain two sub-dictionaries. In recognition phase, sparse coefficients of corresponding training dictionary are derived by solving minimum l1-norm. Finally, the overall reconstruction error is calculated to judge the categories of test samples. In experimental simulation, five kinds of grasp gesture are collected to create gesture sample library. After selecting optimal HOG parameters and the weight of two features, the recognition effect of the method is analysed. Compared with the commonly used classification, the results show that the method has better recognition rate and robustness. Copyright © 2016 Inderscience Enterprises Ltd.
引用
收藏
页码：348 / 356
页数：8
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