Information set based features for the speed invariant gait recognition

A novel speed-invariant gait features called two-fold information set (2FInS) features that capture both spatial and temporal variations in a gait cycle are proposed in this study. These features are obtained by applying first histogram of oriented gradients descriptors on the gait images followed by the representation of the underlying possibilistic uncertainty using the Hanman–Jeevan entropy function. The 2FInS features are validated on three databases: CASIA-C, OU-ISIR Treadmill-A and OU-ISIR Treadmill-D using Procrustes distance based classifier. In view of accounting both spatial and temporal information distributed throughout a gait cycle, the results obtained are superior to those of the existing methods.