Accurate individual identification is essential for monitoring rare amphibians, yet invasive marking is often unsuitable for critically endangered species. We evaluate state-of-the-art computer-vision methods for photographic re-identification of the Hula painted frog (Latonia nigriventer) using 1,233 ventral images from 191 individuals collected during 2013-2020 capture-recapture surveys. We compare deep local-feature matching in a zero-shot setting with deep global-feature embedding models. The local-feature pipeline achieves 98% top-1 closed-set identification accuracy, outperforming all global-feature models; fine-tuning improves the best global-feature model to 60% top-1 (91% top-10) but remains below local matching. To combine scalability with accuracy, we implement a two-stage workflow in which a fine-tuned global-feature model retrieves a short candidate list that is re-ranked by local-feature matching, reducing end-to-end runtime from 6.5-7.8 hours to ~38 minutes while maintaining ~96% top-1 closed-set accuracy on the labeled dataset. Separation of match scores between same- and different-individual pairs supports thresholding for open-set identification, enabling practical handling of novel individuals. We deploy this pipeline as a web application for routine field use, providing rapid, standardized, non-invasive identification to support conservation monitoring and capture-recapture analyses. Overall, in this species, zero-shot deep local-feature matching outperformed global-feature embedding and provides a strong default for photo-identification.

翻译：对稀有两栖动物进行准确个体识别对于监测至关重要，然而侵入性标记方法通常不适用于极度濒危物种。本研究评估了最先进的计算机视觉方法在胡拉彩蛙（Latonia nigriventer）照片重识别中的应用，使用了2013-2020年捕获-重捕获调查中收集的191只个体共1,233张腹面图像。我们在零样本设置下比较了深度局部特征匹配方法与深度全局特征嵌入模型。局部特征流程实现了98%的top-1封闭集识别准确率，优于所有全局特征模型；微调将最佳全局特征模型提升至60%的top-1准确率（91%的top-10），但仍低于局部匹配方法。为兼顾可扩展性与准确性，我们实现了两阶段工作流程：通过微调的全局特征模型检索短候选列表，再由局部特征匹配进行重排序，从而将端到端运行时间从6.5-7.8小时缩短至约38分钟，同时在标注数据集上保持约96%的top-1封闭集准确率。相同个体与不同个体对之间的匹配分数分离支持开放集识别的阈值设定，实现了对新个体的实际处理。我们将该流程部署为网络应用程序供日常野外使用，为保护监测和捕获-重捕获分析提供快速、标准化、非侵入性的识别方案。总体而言，对于该物种，零样本深度局部特征匹配优于全局特征嵌入方法，为照片识别提供了可靠的默认方案。