  Character re-identification, recognizing characters consistently across
different panels in comics, presents significant challenges due to limited
annotated data and complex variations in character appearances. To tackle this
issue, we introduce a robust semi-supervised framework that combines metric
learning with a novel 'Identity-Aware' self-supervision method by contrastive
learning of face and body pairs of characters. Our approach involves processing
both facial and bodily features within a unified network architecture,
facilitating the extraction of identity-aligned character embeddings that
capture individual identities while preserving the effectiveness of face and
body features. This integrated character representation enhances feature
extraction and improves character re-identification compared to
re-identification by face or body independently, offering a parameter-efficient
solution. By extensively validating our method using in-series and inter-series
evaluation metrics, we demonstrate its effectiveness in consistently
re-identifying comic characters. Compared to existing methods, our approach not
only addresses the challenge of character re-identification but also serves as
a foundation for downstream tasks since it can produce character embeddings
without restrictions of face and body availability, enriching the comprehension
of comic books. In our experiments, we leverage two newly curated datasets: the
'Comic Character Instances Dataset', comprising over a million character
instances and the 'Comic Sequence Identity Dataset', containing annotations of
identities within more than 3000 sets of four consecutive comic panels that we
collected.
