  Designing useful person re-identification systems for real-world applications
requires attention to operational aspects not typically considered in academic
research. Here, we focus on the temporal aspect of re-identification; that is,
instead of finding a match to a probe person of interest in a fixed candidate
gallery, we consider the more realistic scenario in which the gallery is
continuously populated by new candidates over a long time period. A key
question of interest for an operator of such a system is: how long is a correct
match to a probe likely to remain in a rank-k shortlist of possible candidates?
We propose to distill this information into a Rank Persistence Curve (RPC),
which allows different algorithms' temporal performance characteristics to be
directly compared. We present examples to illustrate the RPC using a new
long-term dataset with multiple candidate reappearances, and discuss
considerations for future re-identification research that explicitly involves
temporal aspects.
