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Gait Recognition Using Independent Component Analysis

  • Conference paper
Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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Abstract

This paper presents a new method for automatic gait recognition using independent component analysis (ICA). Firstly, a simple background subtraction algorithm is introduced to segment the moving figures accurately and to achieve binary silhouettes. Secondly, these 2D binary silhouettes are converted into associated sequences of 1D signals and ICA is applied to get the independent components of each 2D binary silhouettes. For the sake of reducing computation cost, a fast and robust fixed-point algorithm named FastICA is adopted. A criterion that not all ICs are useful for recognition is demonstrated and a method of IC selection is put forward. Lastly, the nearest neighbor (NN) classifier for recognition is chosen. This algorithm is tested on small MUD gait database and the NLPR gait database and experimental results show that our method has encouraging recognition accuracy.

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Author information

Authors and Affiliations

  1. Department of Information Science, Xi’an University of Technology, Xi’an, Shaanxi, 710048, China

    Jiwen Lu, Erhu Zhang, Zhigang Zhang & Yanxue Xue

Authors
  1. Jiwen Lu
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  2. Erhu Zhang
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  3. Zhigang Zhang
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  4. Yanxue Xue
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

Lu, J., Zhang, E., Zhang, Z., Xue, Y. (2005). Gait Recognition Using Independent Component Analysis. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_29

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  • DOI: https://doi.org/10.1007/11427445_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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