Multiple-shot person re-identification via fair set-collaboration metric learning

As an issue that attracts increasing interests in both academia and industry, multiple-shot person re-identification has shown promising results but suffers from real-scenario complexities and feature-crafting heuristics. To tackle the problems of set-level data variation and sparseness during re-identification, this paper proposes a novel metric learning method, named “Fair Set-Collaboration Metric Learning”, motivated by utilizing the opportunities whilst overcoming the challenges from the set of multiple instances. This method optimizes a new set-collaboration dissimilarity measure, which introduces the fairness principle into the collaborative representation based set to sets distance, in the set based metric learning framework. Experiments on widely-used benchmark datasets have demonstrated the advantages of this method in terms of effectiveness and robustness.