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
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.
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)
Corresponding Authors:
WANG Xiuhui, Ph.D., associate professor. His research interests include pattern recognition, computer vision and computer graphics.
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 
