# Individual common dolphin identification via metric embedding learning

## TL;DR

This paper introduces a metric embedding learning approach using triplet loss for individual dolphin identification, enabling accurate recognition of new dolphins with limited training data and robust performance amidst distractors.

## Contribution

It presents a novel application of triplet loss-based metric learning for dolphin photo-id, achieving high accuracy and generalization to unseen individuals with small datasets.

## Key findings

- Achieved 90.5% top-1 accuracy on test set of 37 dolphins.
- Maintained 93.6% top-5 accuracy, demonstrating effective recognition.
- Performance remained robust with minimal accuracy drop in the presence of distractors.

## Abstract

Photo-identification (photo-id) of dolphin individuals is a commonly used technique in ecological sciences to monitor state and health of individuals, as well as to study the social structure and distribution of a population. Traditional photo-id involves a laborious manual process of matching each dolphin fin photograph captured in the field to a catalogue of known individuals. We examine this problem in the context of open-set recognition and utilise 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 $90.5\pm2$ and $93.6\pm1$ percent. In the presence of 1200 distractors, top-1 accuracy dropped by $12\%$; however, top-5 accuracy saw only a $2.8\%$ drop