Combining feature aggregation and geometric similarity for re-identification of patterned animals

TL;DR

This paper presents a novel animal re-identification method combining pattern appearance and geometric similarity metrics, enabling effective identification across diverse pattern types, demonstrated on seals and whale sharks.

Contribution

It introduces a combined similarity approach that leverages local pattern features and geometric consistency for animal re-identification, applicable to various species.

Findings

Achieves promising re-identification accuracy on seals and whale sharks.

Utilizes both local pattern features and geometric analysis for improved identification.

Applicable to a wide range of patterned animal species.

Abstract

Image-based re-identification of animal individuals allows gathering of information such as migration patterns of the animals over time. This, together with large image volumes collected using camera traps and crowdsourcing, opens novel possibilities to study animal populations. For many species, the re-identification can be done by analyzing the permanent fur, feather, or skin patterns that are unique to each individual. In this paper, we address the re-identification by combining two types of pattern similarity metrics: 1) pattern appearance similarity obtained by pattern feature aggregation and 2) geometric pattern similarity obtained by analyzing the geometric consistency of pattern similarities. The proposed combination allows to efficiently utilize both the local and global pattern features, providing a general re-identification approach that can be applied to a wide variety of different pattern types. In the experimental part of the work, we demonstrate that the method achieves promising re-identification accuracies for Saimaa ringed seals and whale sharks.