Two-Stage PCA Extracts Spatiotemporal Features for Gait Recognition

Abstract

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.