4.6 Article

Gait Recognition Method of Underground Coal Mine Personnel Based on Densely Connected Convolution Network and Stacked Convolutional Autoencoder

期刊

ENTROPY
卷 22, 期 6, 页码 -

出版社

MDPI
DOI: 10.3390/e22060695

关键词

underground coal mine personnel; gait recognition; similarity learning; densely connected convolution network; stacked convolutional autoencoder; Two-Stream neural network

资金

  1. National Key R&D Program of China [2016YFC0801800]
  2. Fundamental Research Funds for the Central Universities [2020YJSJD11]
  3. National Natural Science Foundation of China [51674269]

向作者/读者索取更多资源

Biological recognition methods often use biological characteristics such as the human face, iris, fingerprint, and palm print; however, such images often become blurred under the limitation of the complex environment of the underground, which leads to low identification rates of underground coal mine personnel. A gait recognition method via similarity learning named Two-Stream neural network (TS-Net) is proposed based on a densely connected convolution network (DenseNet) and stacked convolutional autoencoder (SCAE). The mainstream network based on DenseNet is mainly used to learn the similarity of dynamic deep features containing spatiotemporal information in the gait pattern. The auxiliary stream network based on SCAE is used to learn the similarity of static invariant features containing physiological information. Moreover, a novel feature fusion method is adopted to achieve the fusion and representation of dynamic and static features. The extracted features are robust to angle, clothing, miner hats, waterproof shoes, and carrying conditions. The method was evaluated on the challenging CASIA-B gait dataset and the collected gait dataset of underground coal mine personnel (UCMP-GAIT). Experimental results show that the method is effective and feasible for the gait recognition of underground coal mine personnel. Besides, compared with other gait recognition methods, the recognition accuracy has been significantly improved.

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