Automatic Gait Recognition via Fourier Descriptors of Deformable Objects

We describe a new method for Automatic Gait Recognition based around the use of Fourier descriptors that model the periodic deformation of human gait. Fourier descriptors have been used successfully in the past to model the boundary of static or moving, rigid-bodied objects, but many objects actually deform in some way as they move. Here we use Fourier descriptors to model not only the object's boundary, but also the spatio-temporal deformations under which the object's boundary is subjected. We applied this new method to the Large Gait Database, compiled at the University of Southampton, and found that the Fourier descriptors obtained for each person appear to be unique and can be used for recognition. Successful recognition rates of over 85% were obtained from the Large Gait Database using only a small set of descriptors.