The annotation of video streams by automatic content analysis is a growing field of research. The possibility of recognising persons appearing in TV shows allows to automatically structure ever-growing video archives. We propose a new descriptor to re-identify persons featured in videos, that is to say, to spot all occurrences of persons throughout a video. Our approach is dynamic as it benefits from motion information contained in videos, whereas the static approaches are solely based on still images. We extract persontracks from videos and match them using a new descriptor and its associated similarity measure: the space-time histogram. The originality of our approach is the integration of temporal data into the descriptor. Experiments show that it provides a better estimation of the similarity between persontracks. Our contribution has been evaluated using a corpus of real life french TV shows broadcasted on BFMTV and LCP TV channels and on some annotated episodes from “Buffy: the Vampire Slayer”. Experimental results show that our approach significantly improves the precision of the re-identification process thanks to the use of the temporal dimension.