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CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-02-03
Cited: 0
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A partition approach for robust gait recognition based on gait template fusion
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Kejun Wang, Liangliang Liu, Xinnan Ding, Kaiqiang Yu, Gang Hu. A partition approach for robust gait recognition based on gait template fusion[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 709-719.
@article{title="A partition approach for robust gait recognition based on gait template fusion",
author="Kejun Wang, Liangliang Liu, Xinnan Ding, Kaiqiang Yu, Gang Hu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="5",
pages="709-719",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000377"
}
%0 Journal Article
%T A partition approach for robust gait recognition based on gait template fusion
%A Kejun Wang
%A Liangliang Liu
%A Xinnan Ding
%A Kaiqiang Yu
%A Gang Hu
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 5
%P 709-719
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000377
TY - JOUR
T1 - A partition approach for robust gait recognition based on gait template fusion
A1 - Kejun Wang
A1 - Liangliang Liu
A1 - Xinnan Ding
A1 - Kaiqiang Yu
A1 - Gang Hu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 5
SP - 709
EP - 719
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000377
Abstract: gait recognition has significant potential for remote human identification, but it is easily influenced by identity-unrelated factors such as clothing, carrying conditions, and view angles. Many gait templates have been presented that can effectively represent gait features. Each gait template has its advantages and can represent different prominent information. In this paper, gait template fusion is proposed to improve the classical representative gait template (such as a gait energy image) which represents incomplete information that is sensitive to changes in contour. We also present a partition method to reflect the different gait habits of different body parts of each pedestrian. The fused template is cropped into three parts (head, trunk, and leg regions) depending on the human body, and the three parts are then sent into the convolutional neural network to learn merged features. We present an extensive empirical evaluation of the CASIA-B dataset and compare the proposed method with existing ones. The results show good accuracy and robustness of the proposed method for gait recognition.
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