by Liu, C and Yan, W-Q
Abstract:
Gait recognition mainly uses different postures of each individual to per-form identity authentication. In the existing methods, the full-cycle gait images are used for feature extraction, but there are problems such as occlusion and frame loss in the actual scene. It is not easy to obtain a full-cycle gait image. Therefore, how to construct a highly efficient gait recognition algorithm framework based on a small number of gait images to improve the efficiency and accuracy of recognition has become the focus of gait recognition research. In this paper, deep neural network CRBM+FC is created. Based on the characteristics of Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) fusion, a method of learning gait recognition from GEI to output is proposed. A brand-new gait recognition algorithm based on layered fu-sion of LBP and HOG is proposed. This paper also proposes a feature learn-ing network, which uses an unsupervised convolutionally constrained Boltzmann machine to train the Gait Energy Images (GEI).
Reference:
Gait recognition using deep learning (Liu, C and Yan, W-Q), Chapter in Handbook of research on multimedia cyber security (Gupta, B, Gupta, D, eds.), IGI Global, 2020.
Bibtex Entry:
@incollection{liu2020gaitlearning,
author = {Liu, C and Yan, W-Q},
booktitle = {Handbook of research on multimedia cyber security},
editor = {Gupta, B and Gupta, D},
pages = {214--226},
publisher = {IGI Global},
school = {USA},
title = {Gait recognition using deep learning},
url = {https://www.igi-global.com/book/handbook-research-multimedia-cyber-security/237827},
year = {2020},
abstract = {Gait recognition mainly uses different postures of each individual to per-form identity authentication. In the existing methods, the full-cycle gait images are used for feature extraction, but there are problems such as occlusion and frame loss in the actual scene. It is not easy to obtain a full-cycle gait image. Therefore, how to construct a highly efficient gait recognition algorithm framework based on a small number of gait images to improve the efficiency and accuracy of recognition has become the focus of gait recognition research. In this paper, deep neural network CRBM+FC is created. Based on the characteristics of Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) fusion, a method of learning gait recognition from GEI to output is proposed. A brand-new gait recognition algorithm based on layered fu-sion of LBP and HOG is proposed. This paper also proposes a feature learn-ing network, which uses an unsupervised convolutionally constrained Boltzmann machine to train the Gait Energy Images (GEI).},
doi = {10.4018/978-1-7998-2701-6.ch011},
isbn = {1799827011},
isbn = {9781799827016},
}