Person re-identification (re-ID) is a significant problem of computer vision with increasing scientific attention. To date, numerous studies have been conducted to improve the accuracy and robustness of person re-ID to meet the practical demands. However, most of the previous efforts concentrated on solving the closed-set variant of the problem, where a query is assumed to always have a correct match within the set of known people (the gallery set). However, this assumption is usually not valid for the industrial re-ID use cases. In this study, we focus on the open-set person re-ID problem, where, in addition to the similarity ranking, the solution is expected to detect the presence or absence of a given query identity within the gallery set. To determine good practices and to assess the practicality of the person re-ID in industrial applications, first, we convert popular closed-set person re-ID datasets into the open-set scenario. Second, we compare performance of eight state-of-the-art closed-set person re-ID methods under the open-set conditions. Third, we experimentally determine the efficiency of using different loss function combinations for the open-set problem. Finally, we investigate the impact of a statistics-driven gallery refinement approach on the open-set person re-ID performance in the low false-acceptance rate (FAR) region, while simultaneously reducing the computational demands of retrieval. Results show an average detection and identification rate increase of 8.38% and 3.39% on the DukeMTMC-reID and Market1501 datasets, respectively, for a FAR of 1%
