Silhouette-Based Gait Recognition via Deterministic Learning

Abstract

In this paper, we present a new silhouette-based gait recognition method via deterministic learning theory. We select four silhouette features which represent the dynamics of gait motion and can more effectively reflect the tiny variance between different gait patterns. The gait recognition approach consists of two phases: a training phase and a test phase. In the training phase, the gait dynamics underlying different individuals’ gaits are locally-accurately approximated by radial basis function (RBF) networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In the test phase, a bank of dynamical estimators is constructed for all the training gait patterns. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L ₁ 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. The test gait pattern similar to one of the training gait patterns can be recognized according to the smallest error principle. Finally, the recognition performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches on the CASIA gait database (Dataset B).