A Dual-stream Network Based on Body Contour Deformation Field for Gait Recognition

HUO Wei; WANG Ke; TANG Jun; WANG Nian; LIANG Dong

doi:10.11999/JEIT231025

Volume 46 Issue 10

Oct. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(10): 4062-4071

HUO Wei, WANG Ke, TANG Jun, WANG Nian, LIANG Dong. A Dual-stream Network Based on Body Contour Deformation Field for Gait Recognition[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4062-4071. doi: 10.11999/JEIT231025

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HUO Wei, WANG Ke, TANG Jun, WANG Nian, LIANG Dong. A Dual-stream Network Based on Body Contour Deformation Field for Gait Recognition[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4062-4071. doi: 10.11999/JEIT231025

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A Dual-stream Network Based on Body Contour Deformation Field for Gait Recognition

doi: 10.11999/JEIT231025

HUO Wei¹,
WANG Ke²,
TANG Jun^{1
,
,},
WANG Nian¹,
LIANG Dong²

1.
School of Electronic and Information Engineering, Anhui University, Hefei 230601, China
2.
School of Internet, Anhui University, Hefei 230039, China

Funds: The National Natural Science Foundation of China (62273001, 61772032), Anhui Provincial Key Research and Development Project (2022k07020006), The Natural Science Research Key Project of Anhui Educational Committee (KJ2021ZD0004)

Received Date: 2023-09-19
Rev Recd Date: 2024-09-04

Available Online: 2024-09-16

Publish Date: 2024-10-30

Abstract

Abstract

Gait recognition is susceptible to external factors such as camera viewpoints, clothing, and carrying conditions, which could lead to performance degradation. To address these issues, the technique of non-rigid point set registration is introduced into gait recognition, which is used to improve the dynamic perception ability of human morphological changes by utilizing the deformation field between adjacent gait frames to represent the displacement of human contours during walking. Accordingly, a dual-flow convolutional neural network-GaitDef exploiting human contour deformation field is proposed in this paper, which consists of deformation field and gait silhouette extraction branches. Besides, a multi-scale feature extraction module is designed for the sparsity of deformation field data to obtain multi-level spatial structure information of the deformation field. A dynamic difference capture module and a context information augmentation module are proposed to capture the changing characteristics of dynamic regions in gait silhouettes and consequently enhance gait representation ability by utilizing context information. The output features of the dual-branch network structure are fused to obtain the final gait representation. Extensive experimental results verify the effectiveness of GaitDef. The average Rank-1 accuracy of GaitDef can achieve 93.5%和68.3% on CASIA-B and CCPG datasets, respectively.
- Gait recognition,
- Point set registration,
- Convolutional Neural Network (CNN),
- Feature fusion

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References(28)

References

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