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The photo-identification of individuals within an animal population is an important objective for the description of the environment itself, for studies relating to the distribution of species and their habitat-use as well as their conservation. The manual analysis of the photos collected by experts is generally very expensive in terms of time and requires specific knowledge. In the field of cetology, photo-identification based on the dorsal fin of individuals is widespread. The software here presented aims to effectively isolate the outline of the dorsal fin, so that it can be used in applications that exploit nicks, notches and other irregularities for the identification and classification process. It implements a series of innovative and specific operations that make the tool applicable to very large datasets with good performances and it has been successfully used for the extraction of the fin outline on dolphin images acquired in the Gulf of Taranto (Northern Ionian Sea - Central Mediterranean Sea).

Photo-identification is one of the best practices to estimate the abundance of cetaceans and, as such, it can help to obtain the biological information necessary to decision-making and actions to preserve the marine environment and its biodiversity. The Risso's dolphin is one of the least-known cetacean species on a global scale, and the distinctive scars on its dorsal fin proved to be extremely useful to photo-identify single individuals. The main novelty of this paper is the development of a new method based on deep learning, called Neural Network Pool (NNPool), and specifically devoted to the photo-identification of Risso's dolphins. This new method also includes the unique function of recognizing unknown vs known dolphins in large datasets with no interaction by the user. Moreover, the new version of DolFin catalogue, collecting Risso's dolphins data and photos acquired between 2013-2018 in the Northern Ionian Sea (Central-eastern Mediterranean Sea), is presented and used here to carry out the experiments. Results have been validated using a further data set, containing new images of Risso's dolphins from the Northern Ionian Sea and the Azores, acquired in 2019. The performance of the NNPool appears satisfying and increases proportionally to the number of images available, thus highlighting the importance of building large-scale data set for the application at hand.