Regularization in metric learning for person re-identification

Metric learning plays a critical role in person re-identification problem. Unfortunately, due to the small size of training data, the metric learning used in this scenario suffers from over-fitting which leads to degenerated performance. In this paper, we investigate the effect of regularization in metric learning for person re-identification. Concretely we formulate the distance function from three perspectives and hence present four different regularized metric learning methods. Experiments on two popular benchmark data sets VIPeR and CUHK01 validate the effectiveness of our proposed regularization approaches.