  Identifying animals from a large group of possible individuals is very
important for biodiversity monitoring and especially for collecting data on a
small number of particularly interesting individuals, as these have to be
identified first before this can be done. Identifying them can be a very
time-consuming task. This is especially true, if the animals look very similar
and have only a small number of distinctive features, like elephants do. In
most cases the animals stay at one place only for a short period of time during
which the animal needs to be identified for knowing whether it is important to
collect new data on it. For this reason, a system supporting the researchers in
identifying elephants to speed up this process would be of great benefit. In
this paper, we present such a system for identifying elephants in the face of a
large number of individuals with only few training images per individual. For
that purpose, we combine object part localization, off-the-shelf CNN features,
and support vector machine classification to provide field researches with
proposals of possible individuals given new images of an elephant. The
performance of our system is demonstrated on a dataset comprising a total of
2078 images of 276 individual elephants, where we achieve 56% top-1 test
accuracy and 80% top-10 accuracy. To deal with occlusion, varying viewpoints,
and different poses present in the dataset, we furthermore enable the analysts
to provide the system with multiple images of the same elephant to be
identified and aggregate confidence values generated by the classifier. With
that, our system achieves a top-1 accuracy of 74% and a top-10 accuracy of 88%
on the held-out test dataset.
