Int. J. of Wireless and Mobile Computing   »   2016 Vol.11, No.4

 

 

Title: Gesture recognition based on sparse representation

 

Authors: Wei Miao; Gongfa Li; Ying Sun; Guozhang Jiang; Jianyi Kong; Honghai Liu

 

Addresses:
College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, UK

 

Abstract: 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.

 

Keywords: human-computer interaction; HCI; gesture recognition; Hu invariant moments; HOG feature; histogram of oriented gradient; sparse representation; K-SVD; singular value decomposition; simulation; grasp gestures.

 

DOI: 10.1504/IJWMC.2016.10003274

 

Int. J. of Wireless and Mobile Computing, 2016 Vol.11, No.4, pp.348 - 356

 

Submission date: 03 Jun 2016
Date of acceptance: 06 Dec 2016
Available online: 14 Feb 2017

 

 

Editors Full text accessAccess for SubscribersPurchase this articleComment on this article