A pool of multiple person re-identification experts

• We introduce a multiple expert person re-identification framework.
• The answers produced by the experts are pooled using probability rules.
• Significant improvements with respect to existing methods are achieved on 3 datasets.
• Performances of single experts are always outperformed by the proposed approach.

The person re-identification problem, i.e. recognizing a person across non-overlapping cameras at different times and locations, is of fundamental importance for video surveillance applications. Due to pose variations, illumination conditions, background clutter, and occlusions, re-identify a person is an inherently difficult problem which is still far from being solved. In this work, inspired by the recent police lineup innovations, we propose a re-identification approach where Multiple Re-identification Experts (MuRE) are trained to reliably match new probes. The answers from all the experts are then combined to achieve a final decision. The proposed method has been evaluated on three datasets showing significant improvements over state-of-the-art approaches.