  With the rise of digital media content production, the need for analyzing
movies and TV series episodes to locate the main cast of characters precisely
is gaining importance.Specifically, Video Face Clustering aims to group
together detected video face tracks with common facial identities. This problem
is very challenging due to the large range of pose, expression, appearance, and
lighting variations of a given face across video frames. Generic pre-trained
Face Identification (ID) models fail to adapt well to the video production
domain, given its high dynamic range content and also unique cinematic style.
Furthermore, traditional clustering algorithms depend on hyperparameters
requiring individual tuning across datasets. In this paper, we present a novel
video face clustering approach that learns to adapt a generic face ID model to
new video face tracks in a fully self-supervised fashion. We also propose a
parameter-free clustering algorithm that is capable of automatically adapting
to the finetuned model's embedding space for any input video. Due to the lack
of comprehensive movie face clustering benchmarks, we also present a
first-of-kind movie dataset: MovieFaceCluster. Our dataset is handpicked by
film industry professionals and contains extremely challenging face ID
scenarios. Experiments show our method's effectiveness in handling difficult
mainstream movie scenes on our benchmark dataset and state-of-the-art
performance on traditional TV series datasets.
