Analyzing human gait using patterns of translation and rotation

Abstract

We analyze gait with the goal of identifying personal characteristics of individuals, such as gender. We use a novel representation to estimate the amount of translation and rotation in small patches throughout the image. Limb motion in a plane can be described using patterns of translation and rotation. We evaluate the usefulness of both rotation and translation to determine gender. Further, we wish to determine whether discrete portions of the gait cycle are best applied for gender recognition. We use independent components analysis to build a dictionary at each phase of the gait cycle. We train a support vector machine to classify male from female using coefficients of independent components. Our experimental results suggest that determinants of gait play an important role in identifying gender. Further rotation and translation contains different information that is useful at different parts of the gait cycle. © 2009 Springer Berlin Heidelberg.