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

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.