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| Cross-View Gait Recognition Method Based on Multi-branch Residual Deep Network |
| HU Shaohui1, WANG Xiuhui1, LIU Yanqiu1 |
| 1. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018 |
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Abstract Convolution neural network based gait recognition cannot make full use of local fine-grained information. To solve the problem, a cross-view gait recognition method based on multi-branch residual deep network is proposed. The multi-branch network is introduced into convolutional neural network to extract features with different granularity in gait contour sequences. Residual learning and multi-scale feature fusion technology are utilized to enhance the feature learning ability of the network. Experimental results on open-accessed gait datasets CASIA-B and OU-MVLP show that the recognition accuracy of the proposed method is higher than that of the existing algorithms.
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Received: 04 December 2020
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| Fund:Key Research and Development Program of Zhe-jiang Province(No.2021c03151), Natural Science Foundation of Zhejiang Province(No.LY20F020018), Scientific Research Project of Education Department of Zhejiang Province (No.Y201636772) |
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Corresponding Authors:
WANG Xiuhui, Ph.D., associate professor. His research interests include pattern recognition, computer vision and computer graphics.
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| About author:: HU Shaohui, master student. His research interests include computer vision and pattern recognition.LIU Yanqiu, master, associate professor. Her research interests include pattern recognition and computer graphics. |
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2021, Vol. 34 
