Is Re-ranking Useful for Open-set Person Re-identification?

Re-ranking algorithms can often boost the performance of close-set person re-identification. However, limited efforts have been devoted to answering whether a similar conclusion could be derived on open-set person re-identification. Considering that open-set scenario is more practical in real applications, in this paper, we try to answer this question and do a benchmark study of re-ranking on open-set person re-identification. Specifically, we evaluate three feature descriptors, namely MB-LBP, LOMO, and IDE, and four distance metrics, namely Euclidean, Cosine, RRDA, and XQDA, with their combinations as baseline algorithms. Then, we evaluate four popular re-ranking algorithms, including k-reciprocal Encoding, ECN-3, ECN-4, and DaF. Through extensive benchmark studies on the OPeRIDv1.0 dataset, the results show that re-ranking algorithms, though useful for closed-set person re-identification, are not generally effective for the open-set person re-identification problem. We argue that this is because re-ranking algorithms change the score distributions per query, and hence disrupt the FAR estimation across all queries. Accordingly, we propose to align the re-ranking scores to the original score via the min-max normalization, which verifies our hypothesis above.