Gait shape estimation for identification

A method is presented for identifying individuals by shape, given a sequence of noisy silhouettes segmented from video. A spectral partitioning framework is used to cluster similar poses and automatically extract gait shapes. The method uses a variance-weighted similarity metric to induce clusters that cover disparate stages in the gait cycle. This technique is applied to the HumanID Gait Challenge dataset to measure the quality of the shape model, and the efficacy of shape statistics in human identification.