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00925njm 22002777a 4500 dc Kuncheva, Ludmila I. . author Garrido Labrador, José Luis author Ramos Pérez, Ismael author Hennessey, Samuel L. author Rodríguez Diez, Juan José author 2023-05 In the face of the global concern about climate change and endangered ecosystems, monitoring individual animals is of paramount importance. Computer vision methods for animal recognition and re-identification from video or image collections are a modern alternative to more traditional but intrusive methods such as tagging or branding. While there are many studies reporting results on various animal re-identification databases, there is a notable lack of comparative studies between different classification methods. In this paper we offer a comparison of 25 classification methods including linear, non-linear and ensemble models, as well as deep learning networks. Since the animal databases are vastly different in characteristics and difficulty, we propose an experimental protocol that can be applied to a chosen data collections. We use a publicly available database of five video clips, each containing multiple identities (9 to 27), where the animals are typically present as a group in each video frame. Our experiment involves five data representations: colour, shape, texture, and two feature spaces extracted by deep learning. In our experiments, simpler models (linear classifiers) and just colour feature space gave the best classification accuracy, demonstrating the importance of running a comparative study before resorting to complex, time-consuming, and potentially less robust methods. 1574-9541 http://hdl.handle.net/10259/7541 10.1016/j.ecoinf.2023.101994 Animal re-identification Computer vision Classification Convolutional networks Comparative study An experiment on animal re-identification from video