  Visual identification of individual animals that bear unique natural body
markings is an important task in wildlife conservation. The photo databases of
animal markings grow larger and each new observation has to be matched against
thousands of images. Existing photo-identification solutions have constraints
on image quality and appearance of the pattern of interest in the image. These
constraints limit the use of photos from citizen scientists. We present a novel
system for visual re-identification based on unique natural markings that is
robust to occlusions, viewpoint and illumination changes. We adapt methods
developed for face re-identification and implement a deep convolutional neural
network (CNN) to learn embeddings for images of natural markings. The distance
between the learned embedding points provides a dissimilarity measure between
the corresponding input images. The network is optimized using the triplet loss
function and the online semi-hard triplet mining strategy. The proposed
re-identification method is generic and not species specific. We evaluate the
proposed system on image databases of manta ray belly patterns and humpback
whale flukes. To be of practical value and adopted by marine biologists, a
re-identification system needs to have a top-10 accuracy of at least 95%. The
proposed system achieves this performance standard.
