# Perspectives on individual animal identification from biology and computer vision

arXiv: https://arxiv.org/abs/2103.00560 · HTML: https://mlsanity.com/paper/2103.00560/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 137 references that are in the map.
- First posted on arXiv 28 Feb 2021.

## Builds on (most cited first, 30 of 42)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Re-ranking Person Re-identification with k-reciprocal Encoding](https://mlsanity.com/paper/1701.08398/) (2017)
- [HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation](https://mlsanity.com/paper/1908.10357/) (2019)
- [Return of the Devil in the Details: Delving Deep into Convolutional Nets](https://mlsanity.com/paper/1405.3531/) (2014)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [Camera Style Adaptation for Person Re-identification](https://mlsanity.com/paper/1711.10295/) (2017)
- [Improved Embeddings with Easy Positive Triplet Mining](https://mlsanity.com/paper/1904.04370/) (2019)
- [A Survey on Metric Learning for Feature Vectors and Structured Data](https://mlsanity.com/paper/1306.6709/) (2013)
- [Recent Advances in Deep Learning for Object Detection](https://mlsanity.com/paper/1908.03673/) (2019)
- [Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data](https://mlsanity.com/paper/1811.07749/) (2018)
- [Spatial-Temporal Person Re-identification](https://mlsanity.com/paper/1812.03282/) (2018)
- [Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection](https://mlsanity.com/paper/1912.03538/) (2019)
- [Synthetic Examples Improve Generalization for Rare Classes](https://mlsanity.com/paper/1904.05916/) (2019)
- [Towards Automatic Wild Animal Monitoring: Identification of Animal Species in Camera-trap Images using Very Deep Convolutional Neural Networks](https://mlsanity.com/paper/1603.06169/) (2016)
- [Clustering Millions of Faces by Identity](https://mlsanity.com/paper/1604.00989/) (2016)
- [Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning](https://mlsanity.com/paper/2006.09205/) (2020)
- [Wildbook: Crowdsourcing, computer vision, and data science for conservation](https://mlsanity.com/paper/1710.08880/) (2017)

## Built on by (most cited first, 30 of 31)

- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [The iWildCam 2021 Competition Dataset](https://mlsanity.com/paper/2105.03494/) (2021)
- [WildlifeDatasets: An open-source toolkit for animal re-identification](https://mlsanity.com/paper/2311.09118/) (2023)
- [SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification](https://mlsanity.com/paper/2211.10307/) (2022)
- [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)
- [Computer Vision for Primate Behavior Analysis in the Wild](https://mlsanity.com/paper/2401.16424/) (2024)
- [WildFusion: Individual Animal Identification with Calibrated Similarity Fusion](https://mlsanity.com/paper/2408.12934/) (2024)
- [ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification](https://mlsanity.com/paper/2106.15083/) (2021)
- [Human-in-the-Loop Visual Re-ID for Population Size Estimation](https://mlsanity.com/paper/2312.05287/) (2023)
- [Animal Identification with Independent Foreground and Background Modeling](https://mlsanity.com/paper/2408.12930/) (2024)
- [Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification](https://mlsanity.com/paper/2204.10905/) (2022)
- [OpenAnimals: Revisiting Person Re-Identification for Animals Towards Better Generalization](https://mlsanity.com/paper/2410.00204/) (2024)
- [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)
- [Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: how does it work and how can we improve it?](https://mlsanity.com/paper/2406.03356/) (2024)
- [A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset](https://mlsanity.com/paper/2509.12047/) (2025)
- [Towards Zero-Shot Camera Trap Image Categorization](https://mlsanity.com/paper/2410.12769/) (2024)
- [DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation](https://mlsanity.com/paper/2107.07330/) (2021)
- [Learning-based Monocular 3D Reconstruction of Birds: A Contemporary Survey](https://mlsanity.com/paper/2207.04512/) (2022)
- [Combining feature aggregation and geometric similarity for re-identification of patterned animals](https://mlsanity.com/paper/2308.06335/) (2023)
- [Adapting the re-ID challenge for static sensors](https://mlsanity.com/paper/2412.00290/) (2024)
- [Degradation-based augmented training for robust individual animal re-identification](https://mlsanity.com/paper/2603.04163/) (2026)
- [SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification](https://mlsanity.com/paper/2311.05524/) (2023)
- [Enhancing Understanding Through Wildlife Re-Identification](https://mlsanity.com/paper/2405.11112/) (2024)
- [Categorical Keypoint Positional Embedding for Robust Animal Re-Identification](https://mlsanity.com/paper/2412.00818/) (2024)
- [Re-Identifying K=ak=a with AI-Automated Video Key Frame Extraction](https://mlsanity.com/paper/2510.08775/) (2025)
- [Near-perfect photo-ID of the Hula painted frog with zero-shot deep local-feature matching](https://mlsanity.com/paper/2601.08798/) (2026)
- [From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identification](https://mlsanity.com/paper/2603.02270/) (2026)
- [Centering Ecological Goals in Automated Identification of Individual Animals](https://mlsanity.com/paper/2604.20626/) (2026)

## Abstract (arXiv)

Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations and propose how they might be addressed in the future.