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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 1
291-295

Gait virtual sample generation method based on CNN and DLTL

Zhi Shuangshuang1
Zhao Qinghui2
Jin Dahai1
Tang Jin2
1. Engineering Training Center, Xi'an Polytechnic University, Xi'an 710048, China
2. School of Information Science & Engineering, Central South University, Changsha 410083, China
DOI: 10.19734/j.issn.1001-3695.2018.05.0504

Abstract

To solve the problem of small sample of gait recognition in the field of counterterrorism and security issues, this paper proposed a novel gait virtual sample generation method based on deep CNN and DLTL. Firstly, it extracted gait style feature map based on low-level of CNN model VGG19, and then it used the DL to carry on the style feature training. Thus it made style feature model. Moreover, high-level of VGG19 extracted gait context feature map, and then it used the TL to make context feature map carry on the style characteristic learning. Finally, it obtained the virtual migration samples. Experimental results demonstrate that these virtual samples remain individual gait feature but style feature. So this method can effectively expand small sample size. At the same time, when the number of virtual samples increase to a certain number, gait recognition rate has improved. Compared with the existing virtual sample generation method, the method has a better performance, which can generate virtual samples in large numbers and improve the recognition rate of gait recognition steadily.

Foundation Support

国家自然科学基金重大研究计划集成项目(91220301)
国家自然科学基金资助项目(61502537)
湖南省自然科学基金资助项目(2016JJ2150)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.05.0504
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 1
Section: Technology of Graphic & Image
Pages: 291-295
Serial Number: 1001-3695(2020)01-062-0291-05

Publish History

[2020-01-05] Printed Article

Cite This Article

支双双, 赵庆会, 金大海, 等. 基于CNN和DLTL的步态虚拟样本生成方法 [J]. 计算机应用研究, 2020, 37 (1): 291-295. (Zhi Shuangshuang, Zhao Qinghui, Jin Dahai, et al. Gait virtual sample generation method based on CNN and DLTL [J]. Application Research of Computers, 2020, 37 (1): 291-295. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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