Salient feature based graph matching for person re-identification

We propose a person re-identification non-learning based approach that uses symmetry principles, as well as structural relations among salient features. The idea comes from the consideration that local symmetries, at different scales, also enforced by texture features, are potentially more invariant to large appearance changes than lower-level features such as SIFT, ASIFT. Finally, we formulate the re-identification problem as a graph matching problem, where each person is represented by a graph aimed not only at rejecting erroneous matches but also at selecting additional useful ones. Experimental results on public dataset i-LIDS provide good performance compared to state-of-the-art results