基于双流神经网络的煤矿井下人员步态识别方法

人脸、指纹和虹膜等生物识别方法在井下复杂环境限制下常常比较模糊，导致基于这些生物特征的煤矿井下人员身份识别率不高。本文在残差神经网络和栈式卷积自动编码器的基础上，提出了一种基于双流神经网络(TS-GAIT)的步态识别方法。主要利用残差神经网络提取步态模式中包含时空信息的动态特征，利用栈式卷积自动编码器提取包含生理信息的静态特征，并采用一种新颖的特征融合方法实现动态特征和静态特征的融合表征。提取的特征对角度、衣着和携带条件具有鲁棒性。在CASIA-B步态数据集和采集的煤矿工人步态数据集(CM-GAIT)上对该方法进行实验评估。结果表明，采用该方法进行煤矿井下人员步态识别是有效可行的，与其他步态识别方法相比准确率有显著提高。

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.