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Dual Phase Learning for Large Scale Video Gait Recognition

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Advances in Multimedia Modeling (MMM 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5916))

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Abstract

Accurate gait recognition from video is a complex process involving heterogenous features, and is still being developed actively. This article introduces a novel framework, called GC2F, for effective and efficient gait recognition and classification. Adopting a ”refinement-and-classification” principle, the framework comprises two components: 1) a classifier to generate advanced probabilistic features from low level gait parameters; and 2) a hidden classifier layer (based on multilayer perceptron neural network) to model the statistical properties of different subject classes. To validate our framework, we have conducted comprehensive experiments with a large test collection, and observed significant improvements in identification accuracy relative to other state-of-the-art approaches.

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Author information

Authors and Affiliations

  1. School of Information Systems, Singapore Management University,  

    Jialie Shen & HweeHwa Pang

  2. School of Computer Engineering, Nanyang Technological University, Singapore

    Dacheng Tao

  3. Birkbeck College, University of London, UK

    Xuelong Li

Authors
  1. Jialie Shen
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  2. HweeHwa Pang
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  3. Dacheng Tao
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  4. Xuelong Li
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Editor information

Editors and Affiliations

  1. University of Oldenburg, Germany

    Susanne Boll

  2. University of Texas at San Antonio,, TX, San Antonio, USA

    Qi Tian

  3. Microsoft Research Asia, Beijing, P.R. China

    Lei Zhang

  4. Southwest University, Beibei, Chongqing, China

    Zili Zhang

  5. School of Engineering and Information Technology, Deakin University, 221 Burwood Highway, Vic, 3125, Australia

    Yi-Ping Phoebe Chen

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© 2010 Springer-Verlag Berlin Heidelberg

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Shen, J., Pang, H., Tao, D., Li, X. (2010). Dual Phase Learning for Large Scale Video Gait Recognition. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_50

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  • DOI: https://doi.org/10.1007/978-3-642-11301-7_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11300-0

  • Online ISBN: 978-3-642-11301-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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