# Similarity Learning Networks for Animal Individual Re-Identification -- Beyond the Capabilities of a Human Observer

arXiv: https://arxiv.org/abs/1902.09324 · HTML: https://mlsanity.com/paper/1902.09324/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2019.

## Built on by (most cited first, 18 of 18)

- [Perspectives on individual animal identification from biology and computer vision](https://mlsanity.com/paper/2103.00560/) (2021)
- [SealID: Saimaa ringed seal re-identification dataset](https://mlsanity.com/paper/2206.02260/) (2022)
- [ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification](https://mlsanity.com/paper/2106.15083/) (2021)
- [Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification](https://mlsanity.com/paper/2204.10905/) (2022)
- [EDEN: Deep Feature Distribution Pooling for Saimaa Ringed Seals Pattern Matching](https://mlsanity.com/paper/2105.13979/) (2021)
- [The Animal ID Problem: Continual Curation](https://mlsanity.com/paper/2106.10377/) (2021)
- [NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregation](https://mlsanity.com/paper/2206.02498/) (2022)
- [MOTCOM: The Multi-Object Tracking Dataset Complexity Metric](https://mlsanity.com/paper/2207.10031/) (2022)
- [Towards Individual Grevy's Zebra Identification via Deep 3D Fitting and Metric Learning](https://mlsanity.com/paper/2206.02261/) (2022)
- [A contrastive learning approach for individual re-identification in a wild fish population](https://mlsanity.com/paper/2301.00596/) (2023)
- [Deep Visual-Genetic Biometrics for Taxonomic Classification of Rare Species](https://mlsanity.com/paper/2305.06695/) (2023)
- [Recurrence over Video Frames (RoVF) for the Re-identification of Meerkats](https://mlsanity.com/paper/2406.13002/) (2024)
- [ID-Sim: An Identity-Focused Similarity Metric](https://mlsanity.com/paper/2604.05039/) (2026)
- [Visual Microfossil Identification via Deep Metric Learning](https://mlsanity.com/paper/2112.09490/) (2021)
- [An Image Processing Pipeline for Camera Trap Time-Lapse Recordings](https://mlsanity.com/paper/2206.05159/) (2022)
- [Systematic Literature Review of Vision-Based Approaches to Outdoor Livestock Monitoring with Lessons from Wildlife Studies](https://mlsanity.com/paper/2410.05041/) (2024)
- [Efficient Mixture of Geographical Species for On Device Wildlife Monitoring](https://mlsanity.com/paper/2504.08620/) (2025)
- [From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identification](https://mlsanity.com/paper/2603.02270/) (2026)

## Abstract (arXiv)

Deep learning has become the standard methodology to approach computer vision tasks when large amounts of labeled data are available. One area where traditional deep learning approaches fail to perform is one-shot learning tasks where a model must correctly classify a new category after seeing only one example. One such domain is animal re-identification, an application of computer vision which can be used globally as a method to automate species population estimates from camera trap images. Our work demonstrates both the application of similarity comparison networks to animal re-identification, as well as the capabilities of deep convolutional neural networks to generalize across domains. Few studies have considered animal re-identification methods across species. Here, we compare two similarity comparison methodologies: Siamese and Triplet-Loss, based on the AlexNet, VGG-19, DenseNet201, MobileNetV2, and InceptionV3 architectures considering mean average precision (mAP)@1 and mAP@5. We consider five data sets corresponding to five different species: humans, chimpanzees, humpback whales, fruit flies, and Siberian tigers, each with their own unique set of challenges. We demonstrate that Triplet Loss outperformed its Siamese counterpart for all species. Without any species-specific modifications, our results demonstrate that similarity comparison networks can reach a performance level beyond that of humans for the task of animal re-identification. The ability for researchers to re-identify an animal individual upon re-encounter is fundamental for addressing a broad range of questions in the study of population dynamics and community/behavioural ecology. Our expectation is that similarity comparison networks are the beginning of a major trend that could stand to revolutionize animal re-identification from camera trap data.