Individual common dolphin identification via metric embedding learning

Traditional photo-id involves a laborious manual process of matching each dolphin fin photograph captured in the field to a catalogue of known individuals. Weexamine this problem in the context of open-set recognition andutilise a triplet loss function to learn a compact representation of fin images in a Euclidean embedding, where the Euclidean distance metric represents fin similarity. We show that this compact representation can be successfully learnt from a fairly small (in deep learning context) training set and still generalise well to out-of-sample identities (completely new dolphin individuals), with top-1 and top-5 test set (37 individuals) accuracy of 90.5 2 and 93.6 1 percent. In the presence of 1200 distractors, top-1 accuracy dropped by 12%; however, top-5 accuracy saw only a 2.8% drop. I. INTRODUCTION Dolphin photo-identification (photo-ID) studies involve photographing dolphindorsal fins during field work and then having a human categorise images into unique individual animals andmatching them with an existing catalogue of known individuals. Individuals are identified by natural features that can be observed on the fins--these features vary between species, but typically include the pattern of nicks and notches on the trailing edge of the fin, the scratches/rake marks/scars on the fin and (for some species) the pigmentation patterns [1]. Matchingof new images from the field against a large catalogue is time consuming, because it requires a human to compare each candidate image to every fin in the catalogue, taking O(mn) average time (where m is the number of new images and n is the number of catalogued individuals). Moreover, as n increases over time as more individuals are added to the catalogue, so does the likelihood of making a mistake, putting catalogue integrity at risk.