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A Regression Approach for Robust Gait Periodicity Detection with Deep Convolutional Networks

  • Kejun WangEmail author
  • Liangliang Liu
  • Xinnan Ding
  • Yibo Xu
  • Haolin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)

Abstract

This paper presents a regression approach to gait periodicity detection via fitting gait sequence to a sine function by deep convolutional neural networks. The key idea is to model the gait fluctuation as a sinusoidal function because of similar periodic regularity. Each frame of the gait video corresponds to a function value that can represent its periodic features. Convolutional network serves to learn and locate a frame in a gait cycle. To the best of our knowledge, it is the first work based on deep neural networks for gait period detection in the literature. An extensive empirical evaluation is provided on the CASIA-B dataset in terms of different views and network architectures with comparison to the existing works. The results show the good accuracy and robustness of the proposed method for gait periodicity detection.

Keywords

Gait period detection Deep convolutional neural networks Gait recognition Biometrics technology 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kejun Wang
    • 1
    Email author
  • Liangliang Liu
    • 1
  • Xinnan Ding
    • 1
  • Yibo Xu
    • 1
  • Haolin Wang
    • 1
  1. 1.College of AutomationHarbin Engineering UniversityHarbinChina

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