Automated Visual Fin Identification of Individual Great White Sharks

TL;DR

This paper presents the first fully automated contour-based visual identification system for individual great white sharks using dorsal fin imagery, leveraging advanced computer vision techniques for robust fin detection and recognition.

Contribution

It introduces a novel open contour stroke model and a multi-instance recognition framework for automated shark identification, advancing animal biometrics methods.

Findings

Achieved recognition results over thousands of fin images.

Demonstrated robustness of the contour-based approach.

Compared favorably to prior methods.

Abstract

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.