A Multi-view Method for Gait Recognition Using Static Body Parameters

  • Amos Y. Johnson
  • Aaron F. Bobick
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2091)


A multi-view gait recognition method using recovered static body parameters of subjects is presented; we refer to these parameters as activity-specific biometrics. Our data consists of 18 subjects walking at both an angled and frontal-parallel view with respect to the camera. When only considering data from a single view, subjects are easily discriminated; however, discrimination decreases when data across views are considered. To compare between views, we use ground truth motioncapture data of a reference subject to find scale factors that can transform data from different viewsi nto a common frame (“walking-space”). Instead of reporting percent correct from a limited database, we report our results using an expected confusion metric that allows us to predict how our static body parameters filter identity in a large population: lower confusion yields higher expected discrimination power. We show that using motion-capture data to adjust vision data of different views to a common reference frame, we can get achieve expected confusions rates on the order of 6%.


Mutual Information Vision Data View Angle Gait Recognition Reference Subject 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Amos Y. Johnson
    • 1
  • Aaron F. Bobick
    • 2
  1. 1.Electrical and Computer EngineeringGeorgia TechAtlanta
  2. 2.GVU Center/College of ComputingGeorgia TechAtlanta

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