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Photo identification is an essential method to identify cetaceans, by using natural marks over their body, and allows experts to acquire straightforward information on these animals. The importance of cetaceans lies in te fact that they play a crucial role in maintaining the healthiness of marine ecosystems, however they are exposed to several anthropogenic stressors, under which they could collapse with extreme consequences on the marine ecosystem functioning. Hence, obtaining new knowledge on their status is extremely urgent for the marine biodiversity conservation. The smart use of technology to automate the individual recognition can speed up the photo identification process, opening the door to large-scale studies that are manually unfeasible. We performed a systematic review on systems based on machine learning and statistical methods for cetacean photo identification, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. This review highlights that interest has been increasing in recent years and several intelligent systems have been presented. However, there are still some open questions, and further efforts to develop more effective automated systems for cetacean photo identification are recommended.

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.