ISSN : 1796-2048
Volume : 1    Issue : 5    Date : August 2006

Two-Stage PCA Extracts Spatiotemporal Features for Gait Recognition
Sandhitsu R. Das, Robert C. Wilson, Maciej T. Lazarewicz and Leif H. Finkel
Page(s): 9-17
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We propose a technique for gait recognition from motion capture data based on two successive
stages of principal component analysis (PCA) on kinematic data. The first stage of PCA provides a
low dimensional representation of gait. Components of this representation closely correspond to
particular spatiotemporal features of gait that we have shown to be important for visual recognition
of gait in a separate psychophysical study. A second stage of PCA captures the shape of the
trajectory within the low dimensional space during a given gait cycle across different individuals or
gaits. The projection space of the second stage of PCA has distinguishable clusters corresponding
to the individual identity and type of gait. Despite the simple eigen-analysis based approach,
promising recognition performance is obtained.

Index Terms
Gait recognition, principal component analysis, motion features.