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SEALNET: Facial recognition software for ecological studies of harbor seals
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  • Zachary Birenbaum,
  • Hieu Do,
  • Lauren Horstmeyer,
  • Hailey Orff,
  • Krista Ingram,
  • Ahmet Ay
Zachary Birenbaum
Colgate University

Corresponding Author:[email protected]

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Hieu Do
Colgate University
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Lauren Horstmeyer
Colgate University
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Hailey Orff
Colgate University
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Krista Ingram
Colgate University
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Ahmet Ay
Colgate University
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Methods for long-term monitoring of coastal species such as harbor seals, 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 identify, 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). 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 processed 1529 images representing 408 individual seals and achieved 88% (93%) rank-1 accuracy in closed set (open set) seal identification. We identified four seals that were photographed in both years at neighboring haul-out sites, suggesting that some harbor seals exhibit site fidelity within local bays across years, and that there may be evidence of spatial connectivity among haul-out sites. Using capture-mark-recapture (CMR) calculations, we obtained a rough preliminary population estimate of 4386 seals in the Middle Bay area. SealNet software outperformed a similar face recognition method developed for primates, PrimNet, in identifying seals following training on our seal dataset. 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 emerging field of conservation technology.
04 Jan 2022Submitted to Ecology and Evolution
04 Jan 2022Submission Checks Completed
04 Jan 2022Assigned to Editor
06 Jan 2022Reviewer(s) Assigned
31 Jan 2022Review(s) Completed, Editorial Evaluation Pending
02 Feb 2022Editorial Decision: Revise Minor
25 Mar 20221st Revision Received
26 Mar 2022Review(s) Completed, Editorial Evaluation Pending
26 Mar 2022Submission Checks Completed
26 Mar 2022Assigned to Editor
30 Mar 2022Editorial Decision: Accept
May 2022Published in Ecology and Evolution volume 12 issue 5. 10.1002/ece3.8851