Human biometric identification through integration of footprint and gait

Gait recognition has gained attention from the biometric community because it has a couple of advantages over other biometric methods to identify individual humans: (1) it requires no subject contact and (2) gait can be assessed from a distance when other physical measures might be obscured or not available. However, objects carried or worn by a subject, notably a briefcase or overcoat, may deform the gait silhouette and significantly degrade the performance of the gait recognition system. In this paper we propose that footprint and gait information may be combined to create a new method for human identification. This method automatically partitions the gait cycle based on the footprint and fuses these two parameters at the decision level to improve accuracy. We have applied the proposed algorithm to a USF gait data set to demonstrate its performance.