Using Bilinear Models for View-invariant Identity Recognition from Gait
Human identification from gait is a challenging task in realistic surveillance scenarios in which people walking along arbitrary directions are shot by a single camera. In this paper we address the problem of finding an effective view-invariant approach for the human ID from gait problem. Given a dataset of sequences in which different people walking normally are seen from several well-separated views, we learn bilinear models to classify the identity of a known person from a view not included in the available training set. Assuming fixed dynamics, each sequence can be mapped to an observation vector with a constant number of poses using a Markov model. We test our approach on the CMU Mobo database, showing how even adopting a rather compact representation for images, bilinear separation of ID and view outperforms the standard baseline algorithm.