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.
Age determination of common bottlenose dolphins (Tursiops truncatus) using dental radiography pulp:tooth area ratio measurements
