Person re-identification by multiple instance metric learning with impostor rejection

Due to its ability to eliminate the visual ambiguities in single-shot algorithms, video-based person re-identification has received an increasing focus in computer vision. Visual ambiguities caused by variations in view angle, lighting, and occlusions make the re-identification problem extremely challenging. To overcome the ambiguities, most previous approaches often extract robust feature representations or learn a sophisticated feature transformation. However, most of these approaches ignore the effect of the impostors arising from annotation or tracking process. In this case, impostors are regarded as genuine and applied in training process, leading to the model drift problem. In order to reduce the risk of model drifting, we propose to automatically discover impostors in a multiple instance metric learning framework. Specifically, we propose a kNN based confidence score to evaluate how much an impostor invades the interested target and utilize it as a prior in the framework. In the meanwhile, we integrate an impostor rejection mechanism in the multiple instance metric learning framework to automatically discover impostors, and learn the semantical similarity metrics with the refined training set. Experiments show that the proposed system performs favorably against the state-of-the-art algorithms on two challenging datasets (iLIDS-VID and PRID 2011). We have improved the rank 1 recognition rate on iLIDS-VID and PRID 2011 dataset by 1.0% and 1.2%, respectively.