  Deep learning has become the standard methodology to approach computer vision
tasks when large amounts of labeled data are available. One area where
traditional deep learning approaches fail to perform is one-shot learning tasks
where a model must correctly classify a new category after seeing only one
example. One such domain is animal re-identification, an application of
computer vision which can be used globally as a method to automate species
population estimates from camera trap images. Our work demonstrates both the
application of similarity comparison networks to animal re-identification, as
well as the capabilities of deep convolutional neural networks to generalize
across domains. Few studies have considered animal re-identification methods
across species. Here, we compare two similarity comparison methodologies:
Siamese and Triplet-Loss, based on the AlexNet, VGG-19, DenseNet201,
MobileNetV2, and InceptionV3 architectures considering mean average precision
(mAP)@1 and mAP@5. We consider five data sets corresponding to five different
species: humans, chimpanzees, humpback whales, fruit flies, and Siberian
tigers, each with their own unique set of challenges. We demonstrate that
Triplet Loss outperformed its Siamese counterpart for all species. Without any
species-specific modifications, our results demonstrate that similarity
comparison networks can reach a performance level beyond that of humans for the
task of animal re-identification. The ability for researchers to re-identify an
animal individual upon re-encounter is fundamental for addressing a broad range
of questions in the study of population dynamics and community/behavioural
ecology. Our expectation is that similarity comparison networks are the
beginning of a major trend that could stand to revolutionize animal
re-identification from camera trap data.
