Gait recognition method of coal mine personnel based on Two-Stream neural network

Biometric methods such as human faces, fingerprints, and irises are relatively mature, but the images of these biometric methods often become blurred under the limitations of the complex underground environment, which leads to the problem of low identification rate of underground coal mine personnel.To solve this problem, a Two-Stream neural network(TS-GAIT)gait recognition method is proposed based on the residual neural network and the stacked convolutional autoencoder in this paper.The residual neural network is mainly used to extract the dynamic deep features containing spatiotemporal information in the gait pattern.The stacked convolutional autoencoder is used to extract the static invariant features containing physiological information.Moreover, a novel feature fusion method is adopted to achieve the fusion and representation of dynamic and static invariant features.The extracted features are robust to angle, clothing and carrying conditions.The method is evaluated on the challenging CASIA-B gait dataset and the collected gait dataset of coal miners(CM-GAIT).The experimental results show that the method is effective and feasible for gait recognition of underground coal mine personnel.Compared with other gait recognition methods, the accuracy rate has been significantly increased.