  Recent advancements in the automatic re-identification of animal individuals
from images have opened up new possibilities for studying wildlife through
camera traps and citizen science projects. Existing methods leverage distinct
and permanent visual body markings, such as fur patterns or scars, and
typically employ one of two strategies: local features or end-to-end learning.
In this study, we delve into the impact of training set size by conducting
comprehensive experiments across six different methods and five animal species.
While it is well known that end-to-end learning-based methods surpass local
feature-based methods given a sufficient amount of good-quality training data,
the challenge of gathering such datasets for wildlife animals means that local
feature-based methods remain a more practical approach for many species. We
demonstrate the benefits of both local feature and end-to-end learning-based
approaches and show that species-specific characteristics, particularly
intra-individual variance, have a notable effect on training data requirements.
