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Walker-Independent Features for Gait Recognition from Motion Capture Data

  • Michal BalaziaEmail author
  • Petr Sojka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach. Yet in some applications such as video surveillance new identities can appear on the fly and labeled data for all encountered people may not always be available. This work introduces the concept of learning walker-independent gait features directly from raw joint coordinates by a modification of the Fisher’s Linear Discriminant Analysis with Maximum Margin Criterion. Our new approach shows not only that these features can discriminate different people than who they are learned on, but also that the number of learning identities can be much smaller than the number of walkers encountered in the real operation.

Keywords

Singular Value Decomposition Gait Cycle Radial Basis Function Neural Network Identity Class Correct Classification Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Authors thank to the anonymous reviewers for their detailed commentary and suggestions. Data used in this project was created with funding from NSF EIA-0196217 and was obtained from http://mocap.cs.cmu.edu. Our extracted database is available at https://gait.fi.muni.cz/ to support results reproducibility.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Faculty of InformaticsMasaryk UniversityBrnoCzech Republic

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