SEALNET: Facial recognition software for ecological studies of harbor seals
Abstract 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 technol...
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crwiley:10.1002/ece3.8851 2024-06-02T08:13:13+00:00 SEALNET: Facial recognition software for ecological studies of harbor seals Birenbaum, Zach Do, Hieu Horstmyer, Lauren Orff, Hailey Ingram, Krista Ay, Ahmet 2022 http://dx.doi.org/10.1002/ece3.8851 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.8851 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ece3.8851 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Ecology and Evolution volume 12, issue 5 ISSN 2045-7758 2045-7758 journal-article 2022 crwiley https://doi.org/10.1002/ece3.8851 2024-05-03T12:03:02Z Abstract 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. Article in Journal/Newspaper Phoca vitulina Wiley Online Library Middle Bay ENVELOPE(-57.495,-57.495,51.465,51.465) Ecology and Evolution 12 5 |
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English |
description |
Abstract 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 |
Article in Journal/Newspaper |
author |
Birenbaum, Zach Do, Hieu Horstmyer, Lauren Orff, Hailey Ingram, Krista Ay, Ahmet |
spellingShingle |
Birenbaum, Zach Do, Hieu Horstmyer, Lauren Orff, Hailey Ingram, Krista Ay, Ahmet SEALNET: Facial recognition software for ecological studies of harbor seals |
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 |
Wiley |
publishDate |
2022 |
url |
http://dx.doi.org/10.1002/ece3.8851 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.8851 https://onlinelibrary.wiley.com/doi/full-xml/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 |
Ecology and Evolution volume 12, issue 5 ISSN 2045-7758 2045-7758 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1002/ece3.8851 |
container_title |
Ecology and Evolution |
container_volume |
12 |
container_issue |
5 |
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1800736647741964288 |