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

• URL: http://arxiv.org/abs/2512.14878v1
• Date: Tue, 16 Dec 2025 19:47:02 GMT
• Title: Visual-textual Dermatoglyphic Animal Biometrics: A First Case Study on Panthera tigris
• Authors: Wenshuo Li, Majid Mirmehdi, Tilo Burghardt,
• Abstract summary: We extend Re-ID methodologies by incorporating precise dermatoglyphic textual descriptors.<n>We show that these specialist semantics abstract and encode animal coat topology using human-interpretable language tags.<n>We conclude that dermatoglyphic language-guided biometrics can overcome vision-only limitations.
• Score: 11.07566750390282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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