  Re-identification of individual animals in images can be ambiguous due to
subtle variations in body markings between different individuals and no
constraints on the poses of animals in the wild. Person re-identification is a
similar task and it has been approached with a deep convolutional neural
network (CNN) that learns discriminative embeddings for images of people.
However, learning discriminative features for an individual animal is more
challenging than for a person's appearance due to the relatively small size of
ecological datasets compared to labelled datasets of person's identities. We
propose to improve embedding learning by exploiting body landmarks information
explicitly. Body landmarks are provided to the input of a CNN as confidence
heatmaps that can be obtained from a separate body landmark predictor. The
model is encouraged to use heatmaps by learning an auxiliary task of
reconstructing input heatmaps. Body landmarks guide a feature extraction
network to learn the representation of a distinctive pattern and its position
on the body. We evaluate the proposed method on a large synthetic dataset and a
small real dataset. Our method outperforms the same model without body
landmarks input by 26% and 18% on the synthetic and the real datasets
respectively. The method is robust to noise in input coordinates and can
tolerate an error in coordinates up to 10% of the image size.
