Spatio-Temporal Large Margin Nearest Neighbor (ST-LMNN) Based on Riemannian Features for Individual Identification

The major challenge for individual identification from periodical locomotion is to explore the unique spatio-temporal motion characteristic of each individual. In this paper, we present a novel spatio-temporal metric learning approach to activate the discriminating components of Riemannian features. Specifically, to deal with articulated motion embedded in a high dimensional space, we extend the Active Appearance Model to Riemannian manifold to measure the intrinsic variations of poses. Then we extract high order geometric features of each joint, which naturally suggests the biometric signatures of enrolled individuals. To model the most discriminant features in a linear subspace, we propose a Spatio-Temporal Large Margin Nearest Neighbor (ST-LMNN) algorithm to learn the low-dimensional linear embedding in the spatial and temporal domain, respectively. According to the experimental results from two public databases and a new built database, the proposed approach can achieve more accurate identification results in walking and running.