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View Invariant Gait Recognition

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Handbook of Remote Biometrics

Part of the book series: Advances in Pattern Recognition ((ACVPR))

Abstract

Recognition by gait is of particular interest since it is the biometric that is available at the lowest resolution, or when other biometrics are (intentionally) obscured. Gait as a biometric has now shown increasing recognition capability. There are many approaches and these show that recognition can achieve excellent performance on current large databases. The majority of these approaches are planar 2D, largely since the early large databases featured subjects walking in a plane normal to the camera view. To extend deployment capability, we need viewpoint invariant gait biometrics. We describe approaches where viewpoint invariance is achieved by 3D approaches or in 2D. In the first group, the identification relies on parameters extracted from the 3D body deformation during walking. These methods use several video cameras and the 3D reconstruction is achieved after a camera calibration process. On the other hand, the 2D gait biometric approaches use a single camera, usually positioned perpendicular to the subject’s walking direction. Because in real surveillance scenarios a system that operates in an unconstrained environment is necessary, many of the recent gait analysis approaches are orientated toward view-invariant gait recognition.

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Author information

Authors and Affiliations

  1. University of Southampton, Southampton, UK

    Richard D. Seely

  2. University of Southampton, Southampton, UK

    Michela Goffredo

  3. University of Southampton, Southampton, UK

    John N. Carter

  4. University of Southampton, Southampton, UK

    Mark S. Nixon

Authors
  1. Richard D. Seely
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  2. Michela Goffredo
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  3. John N. Carter
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  4. Mark S. Nixon
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Corresponding author

Correspondence to Richard D. Seely .

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Editors and Affiliations

  1. Universitá of Sassari, Dipto. Architettura e Pianificazione, Piazza Duomo, 6, 07041 Alghero SS, Italy

    Massimo Tistarelli (Prof.) (Prof.)

  2. Chinese Academy of Science Institute of Automation, Center Biometrics Research & Security, Room 1227, Zhongguancun 95, 100080 Beijing, China

    Stan Z. Li

  3. University of Maryland, Center for Automation Research, College Park, MD 20742-3275, USA

    Rama Chellappa

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Seely, R.D., Goffredo, M., Carter, J.N., Nixon, M.S. (2009). View Invariant Gait Recognition. In: Tistarelli, M., Li, S.Z., Chellappa, R. (eds) Handbook of Remote Biometrics. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-385-3_3

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  • DOI: https://doi.org/10.1007/978-1-84882-385-3_3

  • Publisher Name: Springer, London

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  • Online ISBN: 978-1-84882-385-3

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