Person re-identification based on re-ranking with expanded k-reciprocal nearest neighbors

Abstract In this paper, a robust re-ranking method based on expanded k-reciprocal neighbors is proposed. Our method assumes that if a gallery image is the probe image of the expanded k-reciprocal nearest neighbors, these images are more likely to be of the same person. Specifically, given a probe image, we replace the probe with its expanded reciprocal nearest neighbor and the final distance is computed by the mean value of the corresponding neighbor set. The proposed method is unsupervised, automatic and applicable to other person re-identification problems. Moreover, our method can perform well even with a simple direct rank list where the Euclidean distance was used to compute the distances between the images. Experiments on many public datasets demonstrate the effectiveness and robustness of our re-ranking method. The proposed method achieves 4.9% improvement in Rank-1 on the CUHK03 dataset and a significant improvement of 18.6% in mAP on the Duke dataset.