Fusion of dynamic and static features for gait recognition over time

Gait recognition aims to identify people at a distance by the way they walk. This paper deals with a problem of recognition by gait when time-dependent covariates are added. Properties of gait can be categorized as static and dynamic features, which we derived from sequences of images of walking subjects. We show that recognition rates fall significantly when gait data is captured over a lengthy time interval. A new fusion algorithm is suggested in the paper wherein the static and dynamic features are fused to obtain optimal performance. The new fusion algorithm divides decision situations into three categories. The first case is when more than two thirds of the classifiers agreed to assign identity to the same class. The second case is when the two different classes are selected by each half of classifiers. The rest falls into the third case. The suggested fusion rule was compared with the most popular fusion rules for biometrics. It is shown that the new fusion rule over-performs the established techniques.