Title: An identity authentication method for ubiquitous electric power internet of things based on dynamic gesture recognition

Authors: Pingping Yu; Jincan Yin; Yi Sun; Zheng Du; Ning Cao

Addresses: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050000, China ' School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050000, China ' State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang, 050000, China ' School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050000, China ' School of Internet of Things and Software Technology, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China

Abstract: This paper presents a novel algorithm for gesture recognition and identity authentication based on continuous hidden Markov model (CHMM) and optical flow method. This study aims to solve the information security problems about ubiquitous electric power internet of things. In this system, the optical flow method is used to segment and extract the features of the preprocessed dynamic gesture information to obtain the features of the dynamic gesture motion track, and the CHMM is chosen to establish a valid user dynamic gesture model, which leads to ensuring the dynamic gestures are accurately recognised. The proposed method is test on accurately recognise the dynamic gestures and the result is compared with the dynamic time warpring (DTW) algorithm and practical swarm optimisation-radial basis function network (PSO-RBFN) algorithm. The result of the comparisons illuminates the superiority of the proposed method in terms of accuracy of identity authentication.

Keywords: dynamic gesture recognition; identity authentication; ubiquitous electric power internet of things; information safety; continuous hidden Markov.

DOI: 10.1504/IJSNET.2021.112889

International Journal of Sensor Networks, 2021 Vol.35 No.1, pp.57 - 67

Received: 23 Mar 2020
Accepted: 27 May 2020
Published online: 08 Feb 2021 *

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