Person re-identification (ReID) has been an active research field for many
years. Despite that, models addressing this problem tend to perform poorly when
the task is to re-identify the same people over a prolonged time, due to
appearance changes such as different clothes and hairstyles. In this work, we
introduce a new method that takes full advantage of the ability of existing
ReID models to extract appearance-related features and combines it with a face
feature extraction model to achieve new state-of-the-art results, both on
image-based and video-based benchmarks. Moreover, we show how our method could
be used for an application in which multiple people of interest, under
clothes-changing settings, should be re-identified given an unseen video and a
limited amount of labeled data. We claim that current ReID benchmarks do not
represent such real-world scenarios, and publish a new dataset, 42Street, based
on a theater play as an example of such an application. We show that our
proposed method outperforms existing models also on this dataset while using
only pre-trained modules and without any further training