  We address the problem of identifying individual cetaceans from images
showing the trailing edge of their fins. Given the trailing edge from an
unknown individual, we produce a ranking of known individuals from a database.
The nicks and notches along the trailing edge define an individual's unique
signature. We define a representation based on integral curvature that is
robust to changes in viewpoint and pose, and captures the pattern of nicks and
notches in a local neighborhood at multiple scales. We explore two ranking
methods that use this representation. The first uses a dynamic programming
time-warping algorithm to align two representations, and interprets the
alignment cost as a measure of similarity. This algorithm also exploits learned
spatial weights to downweight matches from regions of unstable curvature. The
second interprets the representation as a feature descriptor. Feature keypoints
are defined at the local extrema of the representation. Descriptors for the set
of known individuals are stored in a tree structure, which allows us to perform
queries given the descriptors from an unknown trailing edge. We evaluate the
top-k accuracy on two real-world datasets to demonstrate the effectiveness of
the curvature representation, achieving top-1 accuracy scores of approximately
95% and 80% for bottlenose dolphins and humpback whales, respectively.
