SEALNET: Facial recognition software for ecological studies of harbor seals
Methods for long‐term monitoring of coastal species such as harbor seals (Phoca vitulina) are often costly, time‐consuming, and highly invasive, underscoring the need for improved techniques for data collection and analysis. Here, we propose the use of automated facial recognition technology for ide...
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ftpubmed:oai:pubmedcentral.nih.gov:9047973 2023-05-15T17:58:56+02:00 SEALNET: Facial recognition software for ecological studies of harbor seals Birenbaum, Zach Do, Hieu Horstmyer, Lauren Orff, Hailey Ingram, Krista Ay, Ahmet 2022-04-28 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047973/ http://www.ncbi.nlm.nih.gov/pubmed/35505998 https://doi.org/10.1002/ece3.8851 en eng John Wiley and Sons Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047973/ http://www.ncbi.nlm.nih.gov/pubmed/35505998 http://dx.doi.org/10.1002/ece3.8851 © 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. CC-BY Ecol Evol Research Articles Text 2022 ftpubmed https://doi.org/10.1002/ece3.8851 2022-05-08T00:44:49Z Methods for long‐term monitoring of coastal species such as harbor seals (Phoca vitulina) are often costly, time‐consuming, and highly invasive, underscoring the need for improved techniques for data collection and analysis. Here, we propose the use of automated facial recognition technology for identification of individual seals and demonstrate its utility in ecological and population studies. We created a software package, SealNet, that automates photo identification of seals, using a graphical user interface (GUI) software to detect, align, and chip seal faces from photographs and a deep convolutional neural network (CNN) suitable for small datasets (e.g., 100 seals with five photos per seal) to classify individual seals. We piloted the SealNet technology with a population of harbor seals located within Casco Bay on the coast of Maine, USA. Across two years of sampling, 2019 and 2020, at seven haul‐out sites in Middle Bay, we obtained a dataset optimized for the development and testing of SealNet. We processed 1752 images representing 408 individual seals and achieved 88% Rank‐1 and 96% Rank‐5 accuracy in closed set seal identification. In identifying individual seals, SealNet software outperformed a similar face recognition method, PrimNet, developed for primates but retrained on seals. The ease and wealth of image data that can be processed using SealNet software contributes a vital tool for ecological and behavioral studies of marine mammals in the developing field of conservation technology. Text Phoca vitulina PubMed Central (PMC) Middle Bay ENVELOPE(-57.495,-57.495,51.465,51.465) Ecology and Evolution 12 5 |
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Research Articles Birenbaum, Zach Do, Hieu Horstmyer, Lauren Orff, Hailey Ingram, Krista Ay, Ahmet SEALNET: Facial recognition software for ecological studies of harbor seals |
topic_facet |
Research Articles |
description |
Methods for long‐term monitoring of coastal species such as harbor seals (Phoca vitulina) are often costly, time‐consuming, and highly invasive, underscoring the need for improved techniques for data collection and analysis. Here, we propose the use of automated facial recognition technology for identification of individual seals and demonstrate its utility in ecological and population studies. We created a software package, SealNet, that automates photo identification of seals, using a graphical user interface (GUI) software to detect, align, and chip seal faces from photographs and a deep convolutional neural network (CNN) suitable for small datasets (e.g., 100 seals with five photos per seal) to classify individual seals. We piloted the SealNet technology with a population of harbor seals located within Casco Bay on the coast of Maine, USA. Across two years of sampling, 2019 and 2020, at seven haul‐out sites in Middle Bay, we obtained a dataset optimized for the development and testing of SealNet. We processed 1752 images representing 408 individual seals and achieved 88% Rank‐1 and 96% Rank‐5 accuracy in closed set seal identification. In identifying individual seals, SealNet software outperformed a similar face recognition method, PrimNet, developed for primates but retrained on seals. The ease and wealth of image data that can be processed using SealNet software contributes a vital tool for ecological and behavioral studies of marine mammals in the developing field of conservation technology. |
format |
Text |
author |
Birenbaum, Zach Do, Hieu Horstmyer, Lauren Orff, Hailey Ingram, Krista Ay, Ahmet |
author_facet |
Birenbaum, Zach Do, Hieu Horstmyer, Lauren Orff, Hailey Ingram, Krista Ay, Ahmet |
author_sort |
Birenbaum, Zach |
title |
SEALNET: Facial recognition software for ecological studies of harbor seals |
title_short |
SEALNET: Facial recognition software for ecological studies of harbor seals |
title_full |
SEALNET: Facial recognition software for ecological studies of harbor seals |
title_fullStr |
SEALNET: Facial recognition software for ecological studies of harbor seals |
title_full_unstemmed |
SEALNET: Facial recognition software for ecological studies of harbor seals |
title_sort |
sealnet: facial recognition software for ecological studies of harbor seals |
publisher |
John Wiley and Sons Inc. |
publishDate |
2022 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047973/ http://www.ncbi.nlm.nih.gov/pubmed/35505998 https://doi.org/10.1002/ece3.8851 |
long_lat |
ENVELOPE(-57.495,-57.495,51.465,51.465) |
geographic |
Middle Bay |
geographic_facet |
Middle Bay |
genre |
Phoca vitulina |
genre_facet |
Phoca vitulina |
op_source |
Ecol Evol |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047973/ http://www.ncbi.nlm.nih.gov/pubmed/35505998 http://dx.doi.org/10.1002/ece3.8851 |
op_rights |
© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1002/ece3.8851 |
container_title |
Ecology and Evolution |
container_volume |
12 |
container_issue |
5 |
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1766167659312840704 |