In the surveillance field, it is very common to have camera networks covering large crowded areas. Not rarely, cameras in these networks do not share the same field of view and they are not always calibrated. In these cases, common problems such as tracking cannot be directly applied as the information from one camera must be also consistent with the others. This is the most common scenario for the person re-identification problem, where there is the need to detect, track and keep a consistent identification of people across a network of cameras. Many approaches have been developed to solve this problem in different manners.
However, person re-identification is still an open problem due to many challenges required to be addressed to build a robust system. To tackle the re-identification problem and improve the accuracy, we propose a novel approach based on Partial Least Squares signatures, which is based on the visual appearance of people. We demonstrate the method performance with experiments conducted on three public available data sets. Results show that our method overcome the chosen baseline on all data sets.