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CLC number: TP391.4

On-line Access: 2021-05-17

Received: 2020-07-25

Revision Accepted: 2020-11-11

Crosschecked: 2021-02-03

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Liangliang Liu


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.5 P.709-719


A partition approach for robust gait recognition based on gait template fusion

Author(s):  Kejun Wang, Liangliang Liu, Xinnan Ding, Kaiqiang Yu, Gang Hu

Affiliation(s):  College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China

Corresponding email(s):   heukejun@126.com, liuliangliang@hrbeu.edu.cn, dingxinnan@hrbeu.edu.cn, yukaiqiang@hrbeu.edu.cn, hugang@hrbeu.edu.cn

Key Words:  Gait recognition, Partition algorithms, Gait templates, Gait analysis, Gait energy image, Deep convolutional neural networks, Biometrics recognition, Pattern recognition

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.

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A1 - Kejun Wang
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A1 - Xinnan Ding
A1 - Kaiqiang Yu
A1 - Gang Hu
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000377

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.





Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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