Facial recognition is problematic for humans. When it works, it invades privacy and eases us into a surveillance state. When it doesn't work, people have been falsely arrested by police. For bears, it's all good – and facial recognition is now being used to help research, monitor and protect the animals using a neural network-based system called BearID. Normally, that requires methodically examining photographs or physically tagging the animal, as the University of Victoria researcher's work on grizzly behaviour requires being able to pinpoint a specific individual.

San Francisco (CNN Business)It's hard for the average person to tell Dani, Lenore, and Bella apart: They all sport fashionably fuzzy brown coats and enjoy a lot of the same activities, like playing in icy-cold water and, occasionally, ripping apart a freshly caught fish. Melanie Clapham is not the average person. As a bear biologist, she has spent over a decade studying these grizzly bears, who live in Knight Inlet in British Columbia, Canada, and developed a sense for who is who by paying attention to little things that make them different. "I use individual characteristics -- say, one bear has a nick in its ear or a scar on the nose," she said. But Clapham knows most people don't have her eye for detail, and the bears' appearances change dramatically over the course of a year -- such as when they get winter coats and fatten up before denning -- which makes it even harder to distinguish between, say, Toffee and Blonde Teddy.

Researchers say the new technology, termed BearID, created a'non-invasive' technique to study the animals. Despite a decade of behavioural research on grizzly bears in B.C.'s Knight Inlet, Melanie Clapham still has trouble telling some individual bears apart. Brown bears, which include grizzly bears, can change dramatically in their appearance during their younger years and, unlike other wildlife that has spots or stripes, they lack distinguishing markings on their bodies. Ms. Clapham, a conservation biologist and postdoctoral research fellow at the University of Victoria, dreamed of technology that could help her individually identify these furry mammals. While she was looking for a tech team to make that idea possible, south of the border, Ed Miller and Mary Nguyen, two Silicon Valley engineers who are also outdoor and wildlife enthusiasts, had started a project to develop machine-learning models that could be adapted to grizzly bears.

Ed Miller and Mary Nguyen are Silicon Valley software developers by day, but moonlight at solving an unusually fuzzy problem. A few years ago the pair became mesmerized, like many of us, by an Alaskan webcam broadcasting brown bears from Katmai National Park. They also happened to be seeking a project to hone their machine learning expertise. "We thought, machine learning is really great at identifying people, what could it do for bears?" Could artificial intelligence used for face recognition be harnessed to discern one bear face from another?

Grizzly bears have domed shoulders, tall foreheads, and pale-tipped fur that gives them their grizzled appearance. If you're comparing two bears, one might be lighter or darker in color, or fatter for hibernation. But for the most part, there's no universal, unique marker a person can use to tell two bears apart. This issue is a challenge for scientists like University of Victoria wildlife conservationist Melanie Clapham, whose research on grizzly bear behavior requires her to monitor individual bears over years, Adam van der Zwan reports for CBC. But now, Clapham and her research team have developed a solution: facial recognition for bears. Bears grow and shrink a lot depending on the season, and their appearance changes frequently during their 20- to 25-year-long lifespans.

From 4,675 fully labeled bear faces on DSLR photographs, taken from research and bear-viewing sites at Brooks River, Ala., and Knight Inlet, they randomly split images into training and testing data sets. Once trained from 3,740 bear faces, deep learning went to work "unsupervised," Dr. Clapham said, to see how well it could spot differences between known bears from 935 photographs. First, the deep learning algorithm finds the bear face using distinctive landmarks like eyes, nose tip, ears and forehead top. Then the app rotates the face to extract, encode and classify facial features. The system identified bears at an accuracy rate of 84 percent, correctly distinguishing between known bears such as Lucky, Toffee, Flora and Steve.