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.2016.082289

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

Received: 23 Jun 2016
Accepted: 06 Dec 2016
Published online: 16 Feb 2017 *

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