Re-ranking algorithms can often boost the per-
formance of close-set person re-identification. However, limited
efforts have been devoted to answering whether a similar con-
clusion 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 met-
rics, 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.