用文字描述老虎纹路，实现跨模态身份识别。
Visual-textual Dermatoglyphic Animal Biometrics: A First Case Study on Panthera tigris
- 用人类可读的文本标签描述老虎皮毛纹理特征。
- 基于3355张图像中的84,264个细节点，提升跨模态检索准确率。
- 适合生态监测与需要可解释性的动物识别场景。
生物学家长期结合视觉图像与文字笔记进行动物重识别（Re-ID）。现有AI工具多依赖形态特征图像，但难以处理细微差异。本文首次将法医学中用于刻画皮肤纹路的精准文本描述引入生态学，通过人工标注的84,264个细节点（来自3,355张185只老虎的照片），构建了可视-文本融合的重识别方法。该方法以人类可理解的语言标签抽象编码虎皮拓扑结构。为优化性能，我们设计文本-图像联合生成流程，合成数十个逼真‘虚拟个体’，每例包含多个视觉图像与对应文本描述。在真实场景测试中，该增强显著提升跨模态检索精度，缓解数据稀缺问题。结果表明，基于纹路语言引导的生物识别可突破纯视觉局限，实现文本到图像的身份还原，并支持人工验证匹配，推动重识别可解释性发展，为生态监测提供语言驱动的多模态统一方案。
原文摘要 · Abstract (English)
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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