Visual-textual Dermatoglyphic Animal Biometrics: A First Case Study on Panthera tigris

TL;DR

This study introduces a novel visual-textual biometric approach for animal re-identification, combining dermatoglyphic textual descriptors with images to improve accuracy and explainability in ecological monitoring.

Contribution

It pioneers the integration of dermatoglyphic language with visual data for animal re-identification, enhancing cross-modal retrieval and addressing data scarcity issues.

Findings

Textual descriptors improve cross-modal retrieval accuracy.

Virtual individuals boost AI performance in re-identification.

Dermatoglyphic language enables explainable, human-verifiable matches.

Abstract

Biologists have long combined visuals with textual field notes to re-identify (Re-ID) animals. Contemporary AI tools automate this for species with distinctive morphological features but remain largely image-based. Here, we extend Re-ID methodologies by incorporating precise dermatoglyphic textual descriptors-an approach used in forensics but new to ecology. We demonstrate that these specialist semantics abstract and encode animal coat topology using human-interpretable language tags. Drawing on 84,264 manually labelled minutiae across 3,355 images of 185 tigers (Panthera tigris), we evaluate this visual-textual methodology, revealing novel capabilities for cross-modal identity retrieval. To optimise performance, we developed a text-image co-synthesis pipeline to generate 'virtual individuals', each comprising dozens of life-like visuals paired with dermatoglyphic text. Benchmarking against real-world scenarios shows this augmentation significantly boosts AI accuracy in cross-modal retrieval while alleviating data scarcity. We conclude that dermatoglyphic language-guided biometrics can overcome vision-only limitations, enabling textual-to-visual identity recovery underpinned by human-verifiable matchings. This represents a significant advance towards explainability in Re-ID and a language-driven unification of descriptive modalities in ecological monitoring.