Perspectives on Individual Animal Identification from Biology and Computer Vision
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Synopsis: 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.
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Computer vision recently emerged as a powerful alternative for automatic individual identification.
“Similar to counting and reconstruction (see next section), computer vision recently emerged as a powerful alternative for automatic individual identification54,98–100.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [98]
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Flagged first, then by citations
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Cited by
Sorted by citations
Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740 · see citing works
Self-citation
- Authors
- Vidal, M., Wolf, N., Rosenberg, B., Harris, B. P., & Mathis, A.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Alaska Pacific University (US)
- Corresponding author
- Mathis, Alexander
- Venue
- Integrative and Comparative Biology
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-05-29
- Volume
- 61
- Issue
- 3
- Pages
- 900-916
- ISSN
- 1540-7063 (ISSN-L)
- 1557-7023
- PMC
- PMC8490693
- OpenAlex
- W3135744304
- Cited by
- 104
- References
- 128
- Citations by year
- 2026: 19 · 2025: 25 · 2024: 21 · 2023: 22 · 2022: 10 · 2021: 5 · 2020: 2
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Integrative and Comparative Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- At-Sea Processors Association and the Groundfish Forum
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- individual animal identification, computer vision, automatic identification, animal tracking, mark-recapture, image recognition, wildlife monitoring
- MeSH terms
- Animals, Animal Identification Systems, Biology, Computers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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