Gait Recognition Method of Underground Coal Mine Personnel Based on Densely Connected Convolution Network and Stacked Convolutional Autoencoder
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 min...
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doaj-20cf7dcdd76d4b939451b71332a43bdc2020-11-25T03:10:42ZengMDPI AGEntropy1099-43002020-06-012269569510.3390/e22060695Gait Recognition Method of Underground Coal Mine Personnel Based on Densely Connected Convolution Network and Stacked Convolutional AutoencoderXiaoyang Liu0Jinqiang Liu1School of Mechanical Electronic & Information Engineering, China University of Mining & Technology, Beijing 100083, ChinaSchool of Mechanical Electronic & Information Engineering, China University of Mining & Technology, Beijing 100083, ChinaBiological 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.https://www.mdpi.com/1099-4300/22/6/695underground coal mine personnelgait recognitionsimilarity learningdensely connected convolution networkstacked convolutional autoencoderTwo-Stream neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaoyang Liu Jinqiang Liu |
spellingShingle |
Xiaoyang Liu Jinqiang Liu Gait Recognition Method of Underground Coal Mine Personnel Based on Densely Connected Convolution Network and Stacked Convolutional Autoencoder Entropy underground coal mine personnel gait recognition similarity learning densely connected convolution network stacked convolutional autoencoder Two-Stream neural network |
author_facet |
Xiaoyang Liu Jinqiang Liu |
author_sort |
Xiaoyang Liu |
title |
Gait Recognition Method of Underground Coal Mine Personnel Based on Densely Connected Convolution Network and Stacked Convolutional Autoencoder |
title_short |
Gait Recognition Method of Underground Coal Mine Personnel Based on Densely Connected Convolution Network and Stacked Convolutional Autoencoder |
title_full |
Gait Recognition Method of Underground Coal Mine Personnel Based on Densely Connected Convolution Network and Stacked Convolutional Autoencoder |
title_fullStr |
Gait Recognition Method of Underground Coal Mine Personnel Based on Densely Connected Convolution Network and Stacked Convolutional Autoencoder |
title_full_unstemmed |
Gait Recognition Method of Underground Coal Mine Personnel Based on Densely Connected Convolution Network and Stacked Convolutional Autoencoder |
title_sort |
gait recognition method of underground coal mine personnel based on densely connected convolution network and stacked convolutional autoencoder |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2020-06-01 |
description |
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. |
topic |
underground coal mine personnel gait recognition similarity learning densely connected convolution network stacked convolutional autoencoder Two-Stream neural network |
url |
https://www.mdpi.com/1099-4300/22/6/695 |
work_keys_str_mv |
AT xiaoyangliu gaitrecognitionmethodofundergroundcoalminepersonnelbasedondenselyconnectedconvolutionnetworkandstackedconvolutionalautoencoder AT jinqiangliu gaitrecognitionmethodofundergroundcoalminepersonnelbasedondenselyconnectedconvolutionnetworkandstackedconvolutionalautoencoder |
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1724657816403181568 |