Cross-Speed Gait Recognition Using Speed-Invariant Gait Templates and Globality–Locality Preserving Projections

摘要

We present a novel manifold-based approach for cross-speed gait recognition. In our approach, the walking action is considered as residing on a manifold, in the feature space, that is homomorphic to a unit circle. We employ thin plate spline (TPS) kernel-based radial basis function (RBF) interpolation to fit such manifold. TPS kernel-based RBF interpolation separates the learned coefficients into an affine component and a nonaffine component, which, respectively, encodes the dynamic and static characteristics of the gait manifold. We introduce the use of the nonaffine component as a cross-speed gait representation, and denote it speed invariant gait template (SIGT). We also propose an enhanced locality preserving projections (LPP) algorithm named globality LPP (GLPP) for reducing the dimension of SIGT. In GLPP, the graph Laplacians of intrasubject part and intersubjects part are separately constructed, and then to combine as a new graph Laplacian. Finally, a manifold learning-based classifier named normalized hypergraph classifier is employed for classification. Experimental results on two gait databases demonstrate the effectiveness of our proposed approach in comparison with the state-of-the-art gait recognition methods.