  Hoping to stimulate new research in individual animal identification from
images, we propose to formulate the problem as the human-machine Continual
Curation of images and animal identities. This is an open world recognition
problem, where most new animals enter the system after its algorithms are
initially trained and deployed. Continual Curation, as defined here, requires
(1) an improvement in the effectiveness of current recognition methods, (2) a
pairwise verification algorithm that allows the possibility of no decision, and
(3) an algorithmic decision mechanism that seeks human input to guide the
curation process. Error metrics must evaluate the ability of recognition
algorithms to identify not only animals that have been seen just once or twice
but also recognize new animals not in the database. An important measure of
overall system performance is accuracy as a function of the amount of human
input required.
