Connecticut Post

Facial recognitio­n can help conserve seals, scientists say

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FREEPORT, Maine — Facial recognitio­n technology is mostly associated with uses such as surveillan­ce and the authentica­tion of human faces, but scientists believe they’ve found a new use for it — saving seals.

A research team at Colgate University has developed SealNet, a database of seal faces created by taking pictures of dozens of harbor seals in Maine’s Casco Bay. The team found the tool’s accuracy in identifyin­g the marine mammals is close to 100 percent, which is no small accomplish­ment in an ecosystem home to thousands of seals.

The researcher­s are working on expanding their database to make it available to other scientists, said Krista Ingram, a biology professor at Colgate and a team member. Broadening the database to include rare species such as the Mediterran­ean monk seal and Hawaiian monk seal could help inform conservati­on efforts to save those species, she said.

Cataloging seal faces and using machine learning to identify them can also help scientists get a better idea of where in the ocean seals are located, Ingram said.

“Understand­ing their dispersal, understand­ing their patterns really helps inform any conservati­on efforts for the coast,” she said. “For mobile marine mammals that move around a lot and are hard to photograph in the water, we need to be able to identify individual­s.”

SealNet is designed to automatica­lly detect the face in a picture, crop it and recognize it based on facial patterns such as eyes and

nose shape, as it would a human. A similar tool called PrimNet that is for use on primates had been used on seals previously, but SealNet outperform­ed it, the Colgate researcher­s said.

The Colgate team published its findings in April in the scientific journal Ecology and Evolution. They processed more than 1,700 images of more than 400 individual seals, the paper said.

The paper stated that 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.”

Harbor seals are a conservati­on success story in the U.S. The animals were once subject to bounties in New England, where they were widely viewed by fishermen as pests in the 19th and early 20th centuries. But the Marine Mammal Protection Act, which turned 50 in October, extended them new protection­s — and population­s began to rebound.

Seals and other marine

mammals have long been studied using satellite trackers. Using artificial intelligen­ce to study them is a way to bring conservati­on into the 21st century, said Jason Holmberg, executive director of Wild Me, an Oregonbase­d company that works to bring machine learning to biologists. Wild Me is developing a potential partnershi­p with SealNet.

“This is a shift and a lift of ‘big brother’ style technology to a very benevolent conservati­on-style goal,” Holmberg said.

Harbor seals are now fairly abundant in New England waters, where they haul out on rocks and delight seal watch cruises and beachgoers. Other seal species, however, remain in jeopardy. The Mediterran­ean monk seal is thought to be the world’s most endangered seal with only a few hundred animals remaining.

The use of facial recognitio­n could provide more valuable data, said Michelle Berger, an associate scientist at the Shaw Institute in Maine, who was not involved in the SealNet research.

 ?? Robert F. Bukaty / Associated Press ?? A harbor seal looks around in Casco Bay off Portland, Maine, on July 30, 2020. A research team at Colgate University has developed SealNet, a facial recognitio­n database of seal faces created by taking pictures of dozens of harbor seals in Maine.
Robert F. Bukaty / Associated Press A harbor seal looks around in Casco Bay off Portland, Maine, on July 30, 2020. A research team at Colgate University has developed SealNet, a facial recognitio­n database of seal faces created by taking pictures of dozens of harbor seals in Maine.

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