People re-identification using two-stage transfer metric learning

With assumptions that people usually do not change their clothes during an observation period, people appearance data are easily outdated in re-identification applications. This raises the over-fitting problem because only a few training data are available for learning statistical models. In this paper, we propose a two-stage transfer metric learning approach for multiple-shot people re-identification to tackle this small training data problem. In the first stage, we transfer the generic knowledge from a large existing dataset, and in the second stage, we transfer the learned distance metric for each probe-specific person using the side-information. Experimental results on several public benchmark datasets show that our proposed approach is superior over conventional approaches.