Discriminative deep transfer metric learning for cross-scenario person re-identification

A novel discriminative deep transfer learning method called DDTML is proposed for Cross-scenario Person Reidentification( Re-ID). Using a deep neural network, DDTML learns a set of hierarchical nonlinear transformations for Cross-scenario Person Re-identification by transferring discriminative knowledge from the source domain to the target domain. Meanwhile, taking account of the inherent characteristics of Re-ID data sets, in order to reduce the distribution divergence between the source data and the target data, DDTML minimizes a new maximum mean discrepancy based on Class Distribution called MMDCD at the top layer of the network. Experimental results on widely used Re-identification datasets show the effectiveness of the proposed classifiers.