# Automated Visual Fin Identification of Individual Great White Sharks

arXiv: https://arxiv.org/abs/1609.06323 · HTML: https://mlsanity.com/paper/1609.06323/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2016.

## Built on by (most cited first, 11 of 11)

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- [Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification](https://mlsanity.com/paper/2307.15427/) (2023)
- [Unsupervised Pelage Pattern Unwrapping for Animal Re-identification](https://mlsanity.com/paper/2506.15369/) (2025)
- [Pairwise Matching of Intermediate Representations for Fine-grained Explainability](https://mlsanity.com/paper/2503.22881/) (2025)
- [Simplified Concrete Dropout -- Improving the Generation of Attribution Masks for Fine-grained Classification](https://mlsanity.com/paper/2307.14825/) (2023)
- [Understanding the Impact of Training Set Size on Animal Re-identification](https://mlsanity.com/paper/2405.15976/) (2024)
- [Automated Visual Monitoring of Nocturnal Insects with Light-based Camera Traps](https://mlsanity.com/paper/2307.15433/) (2023)
- [Diving with Penguins: Detecting Penguins and their Prey in Animal-borne Underwater Videos via Deep Learning](https://mlsanity.com/paper/2308.07267/) (2023)

## Abstract (arXiv)

This paper discusses the automated visual identification of individual great white sharks from dorsal fin imagery. We propose a computer vision photo ID system and report recognition results over a database of thousands of unconstrained fin images. To the best of our knowledge this line of work establishes the first fully automated contour-based visual ID system in the field of animal biometrics. The approach put forward appreciates shark fins as textureless, flexible and partially occluded objects with an individually characteristic shape. In order to recover animal identities from an image we first introduce an open contour stroke model, which extends multi-scale region segmentation to achieve robust fin detection. Secondly, we show that combinatorial, scale-space selective fingerprinting can successfully encode fin individuality. We then measure the species-specific distribution of visual individuality along the fin contour via an embedding into a global `fin space'. Exploiting this domain, we finally propose a non-linear model for individual animal recognition and combine all approaches into a fine-grained multi-instance framework. We provide a system evaluation, compare results to prior work, and report performance and properties in detail.