Learning speed invariant gait template via thin plate spline kernel manifold fitting

Abstract

We present a novel approach for cross-speed gait recognition. In our approach, the cyclic walking action is considered as residing on a manifold which is homeomorphic to a unit circle in the gait space. Thin Plate Spline (TPS) kernel-based Radial Basis Function (RBF) interpolation is used to fit the walking manifold for each gait sequence. The subject related kernel mapping coefficients are learned for representing the gait. According to the property of TPS, the coefficients can be naturally separated as an affine component and a non-affine component. The affine component is the style factor corresponding to the deformation of the homeomorphic manifold caused by the walking action, while the non-affine component is the shape factor, invariant to the walking speed. We denote this non-affine component as Speed Invariant Gait Template (SIGT) and use it as cross-speed gait feature. To address the curse of dimensionality issue and speed up the recognition, we use Globality Locality Preserving Projections (GLPP) to reduce the dimensions of SIGTs. Two walking speeds related gait databases are employed for evaluating our proposed method. The experimental results demonstrate the superiority of our method over the state-of-the-art.