Gait recognition across different walking speeds via deterministic learning

Deformation of gait silhouettes caused by objects under different walking speeds has a significant effect on the performance of gait recognition. In this paper, we present an algorithm via deterministic learning theory to eliminate the effect of walking speed for efficient gait recognition in the lateral view. Three kinds of silhouette features are selected. They capture the spatio-temporal characteristics of each individual׳s movement and represent the dynamics of gait motion. They also can sensitively reflect the variance between gait patterns under different walking speeds. The gait recognition approach consists of two phases: a training phase and a recognition phase. In the training phase, human gait dynamics underlying different individuals׳ gaits across different walking speeds are locally accurately approximated by radial basis function (RBF) neural networks. Obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In order to handle the problem of speed change no matter the variation is small or significantly large, the training patters under different walking speeds constitute a uniform training dataset containing all kinds of gait dynamics of each individual under different walking speeds. In the recognition phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of human gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern whose speed pattern included in the prior training dataset, a set of recognition errors are generated. The average L1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Finally, comprehensive experiments are carried out on the most well-known public gait databases: the CMU, the OU-ISIR, and the CASIA gait database C to demonstrate the recognition performance of the proposed algorithm.