  Automated animal face identification plays a crucial role in the monitoring
of behaviors, conducting of surveys, and finding of lost animals. Despite the
advancements in human face identification, the lack of datasets and benchmarks
in the animal domain has impeded progress. In this paper, we introduce the
PetFace dataset, a comprehensive resource for animal face identification
encompassing 257,484 unique individuals across 13 animal families and 319 breed
categories, including both experimental and pet animals. This large-scale
collection of individuals facilitates the investigation of unseen animal face
verification, an area that has not been sufficiently explored in existing
datasets due to the limited number of individuals. Moreover, PetFace also has
fine-grained annotations such as sex, breed, color, and pattern. We provide
multiple benchmarks including re-identification for seen individuals and
verification for unseen individuals. The models trained on our dataset
outperform those trained on prior datasets, even for detailed breed variations
and unseen animal families. Our result also indicates that there is some room
to improve the performance of integrated identification on multiple animal
families. We hope the PetFace dataset will facilitate animal face
identification and encourage the development of non-invasive animal automatic
identification methods.
