Recent progress in animal biometrics has revolutionized wildlife research. Cutting-edge techniques allow researchers to track individuals through noninvasive methods of recognition that are not only more reliable, but also applicable to large, hard-to-find, and otherwise difficult to observe animals. In this thesis, we propose a framework for automated individual shark identification based on semantic segmentation, boundary descriptors and bipartite perfect matching applied to shark dorsal fins. In order to identify a shark, we first apply semantic segmentation to extract the dorsal fin of the input source, then transform the fin’s contour to a normalized coordinate system so that we can analyze images of sharks regardless of orientation and scale. Finally, we propose a metric scheme that performs a minimum weight perfect matching in a bipartite graph. The experimental results show that our metric is able to identify and track individuals from visual data.