# Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning

arXiv: https://arxiv.org/abs/2006.09205 · HTML: https://mlsanity.com/paper/2006.09205/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 26 of 26)

- [WildlifeDatasets: An open-source toolkit for animal re-identification](https://mlsanity.com/paper/2311.09118/) (2023)
- [Perspectives on individual animal identification from biology and computer vision](https://mlsanity.com/paper/2103.00560/) (2021)
- [Towards Self-Supervision for Video Identification of Individual Holstein-Friesian Cattle: The Cows2021 Dataset](https://mlsanity.com/paper/2105.01938/) (2021)
- [WildlifeReID-10k: Wildlife re-identification dataset with 10k individual animals](https://mlsanity.com/paper/2406.09211/) (2024)
- [PetFace: A Large-Scale Dataset and Benchmark for Animal Identification](https://mlsanity.com/paper/2407.13555/) (2024)
- [Triple-stream Deep Metric Learning of Great Ape Behavioural Actions](https://mlsanity.com/paper/2301.02642/) (2023)
- [A Systematic Review of Machine Learning Techniques for Cattle Identification: Datasets, Methods and Future Directions](https://mlsanity.com/paper/2210.09215/) (2022)
- [Human-in-the-Loop Visual Re-ID for Population Size Estimation](https://mlsanity.com/paper/2312.05287/) (2023)
- [Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification](https://mlsanity.com/paper/2204.10905/) (2022)
- [Automatic Cattle Identification using YOLOv5 and Mosaic Augmentation: A Comparative Analysis](https://mlsanity.com/paper/2210.11939/) (2022)
- [Universal Bovine Identification via Depth Data and Deep Metric Learning](https://mlsanity.com/paper/2404.00172/) (2024)
- [Holstein-Friesian Re-Identification using Multiple Cameras and Self-Supervision on a Working Farm](https://mlsanity.com/paper/2410.12695/) (2024)
- [Pairwise Matching of Intermediate Representations for Fine-grained Explainability](https://mlsanity.com/paper/2503.22881/) (2025)
- [MOO: A Multi-view Oriented Observations Dataset for Viewpoint Analysis in Cattle Re-Identification](https://mlsanity.com/paper/2603.04314/) (2026)
- [Towards Individual Grevy's Zebra Identification via Deep 3D Fitting and Metric Learning](https://mlsanity.com/paper/2206.02261/) (2022)
- [Deep Visual-Genetic Biometrics for Taxonomic Classification of Rare Species](https://mlsanity.com/paper/2305.06695/) (2023)
- [Public Computer Vision Datasets for Precision Livestock Farming: A Systematic Survey](https://mlsanity.com/paper/2406.10628/) (2024)
- [Degradation-based augmented training for robust individual animal re-identification](https://mlsanity.com/paper/2603.04163/) (2026)
- [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)
- [Systematic Literature Review of Vision-Based Approaches to Outdoor Livestock Monitoring with Lessons from Wildlife Studies](https://mlsanity.com/paper/2410.05041/) (2024)
- [AI-Powered Cow Detection in Complex Farm Environments](https://mlsanity.com/paper/2501.02080/) (2025)
- [8-Calves Image dataset](https://mlsanity.com/paper/2503.13777/) (2025)
- [Not All Birds Look The Same: Identity-Preserving Generation For Birds](https://mlsanity.com/paper/2512.04485/) (2025)
- [Cross-Camera Cow Identification via Disentangled Representation Learning](https://mlsanity.com/paper/2602.07566/) (2026)
- [Advanced Machine Learning and Deep Learning Techniques for Enhanced Cattle Identification and Detection: A Comprehensive Review](https://mlsanity.com/paper/2606.15655/) (2026)

## Abstract (arXiv)

Holstein-Friesian cattle exhibit individually-characteristic black and white coat patterns visually akin to those arising from Turing's reaction-diffusion systems. This work takes advantage of these natural markings in order to automate visual detection and biometric identification of individual Holstein-Friesians via convolutional neural networks and deep metric learning techniques. Existing approaches rely on markings, tags or wearables with a variety of maintenance requirements, whereas we present a totally hands-off method for the automated detection, localisation, and identification of individual animals from overhead imaging in an open herd setting, i.e. where new additions to the herd are identified without re-training. We propose the use of SoftMax-based reciprocal triplet loss to address the identification problem and evaluate the techniques in detail against fixed herd paradigms. We find that deep metric learning systems show strong performance even when many cattle unseen during system training are to be identified and re-identified -- achieving 93.8% accuracy when trained on just half of the population. This work paves the way for facilitating the non-intrusive monitoring of cattle applicable to precision farming and surveillance for automated productivity, health and welfare monitoring, and to veterinary research such as behavioural analysis, disease outbreak tracing, and more. Key parts of the source code, network weights and datasets are available publicly.