Gait Recognition Using Deep Learning

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Author(s): Chaoran Liu (Auckland University of Technology, New Zealand)and Wei Qi Yan (Auckland University of Technology, New Zealand)
Copyright: 2020
Pages: 13
Source title: Handbook of Research on Multimedia Cyber Security
Source Author(s)/Editor(s): Brij B. Gupta (National Institute of Technology, Kurukshetra, India)and Deepak Gupta (LoginRadius Inc., Canada)
DOI: 10.4018/978-1-7998-2701-6.ch011

Keywords: Cyber and Network Security / Information Science Reference / Media & Communications / Multimedia Technology

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Abstract

Gait recognition mainly uses different postures of each individual to perform 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 chapter, 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 chapter also proposes a feature learning network, which uses an unsupervised convolutionally constrained Boltzmann machine to train the Gait Energy Images (GEI).

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Gait Recognition Using Deep Learning

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