Facial recognition technology could save seals. A research team from Colgate University has developed SealNet, a database of seal faces created by photographing dozens of common seals in Casco Bay, Maine, the AP reports.

The team found that the tool's accuracy in identifying marine mammals is close to 100 percent, a significant achievement in an ecosystem that is home to thousands of seals.

The researchers are working to expand their database to make it available to other scientists, said Krista Ingram, a biology professor at Colgate and a member of the team. Expanding the database to include rare species, such as the Mediterranean monk seal and the Hawaiian monk seal, could help with conservation efforts for these species, she said.

Cataloging the faces of seals and using machine learning to identify them could also help scientists better understand where seals live in the ocean, Ingram said.

SealNet is designed to automatically detect a face in an image, crop it and recognize it based on facial features, such as the shape of eyes and nose, like a human. A similar tool called PrimNet, which is designed for use on primates, has previously been used on seals, but SealNet has surpassed it, Colgate researchers say.