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.
Tracking individual bears is important, she explained, because it can help with research and conservation of the species; knowing which bear is which could even help with problems like figuring out if a certain grizzly is getting into garbage cans or attacking a farmer’s livestock. Several years ago Clapham began wondering whether a technology typically used to identify humans might be able to help: facial recognition software, which compares measurements between different facial features in one image to those in another.
Building a grizzly data set
“It does way better than we do,” said Miller.
Facial recognition on the ranch
Beef cattle, he explained, pass through many different people and places during their lives, from producers to pasture operations to feed lots and then to meat packing plants. There isn’t much tracking between them, which makes it hard to investigate problems like animal-based diseases that can devastate livestock and may harm people, too. Hoagland expects the app to be available by the end of the year.
“Being able to trace that diseased animal, find its source, quarantine it, do contact tracing — all the things we’re talking about with coronavirus are things we can do with animals, too,” he said.
Hoagland approached KC Olson, a professor at Kansas State University, who brought together a group of specialists at the school in areas like veterinary science and computer science in order to gather pictures of cattle to create a database for training and testing an AI system. They built a proof-of-concept system in March that included more than 135,000 images of 1,000 young beef cattle; Olson said it was 94% accurate at identifying animals, whether or not it had seen them before.
He said that’s far better than what he’s seen with RFID tags and readers, which can work poorly when cattle are densely packed.
“This is a major leap forward in accuracy,” he said.
Gold for poachers
Although facial recognition for animals isn’t fraught with the same privacy, bias, and surveillance issues as it is for people, there are unique issues to consider.
“What’s great for scientists and conservation managers is also gold for poachers of wildlife,” she said.
That’s because a poacher could use images of animals, coupled with data such as GPS coordinates that may be attached to the photos, to find them.
There’s also the difficulty of collecting a large number of images of individual animals — from multiple viewpoints, in different lighting conditions, without obstructions like plants, taken repeatedly over time — to train AI networks.
Jain, who is no longer working on that project, said gathering sufficient animal photos was particularly tricky — especially with lemurs, who may bunch together in a tree. Facial-recognition networks for humans, he noted, may be trained with millions of photos of hundreds of thousands of people; BearID has relied upon just a fraction as many so far, as did Jain’s research.
Clapham said she has more images of some bears than others, so her team is trying to get more of the bears that are less represented in the dataset. The researchers also want to stfart training their AI system on footage from camera traps, which are cameras equipped with a sensor and lights and placed in the wilderness where animals may wander by and trigger video recordings. They’re considering how BearID could go beyond bears to other animals as well.
“Really any species we can get good training data for we should potentially be able to develop this type of facial recognition for as well,” Clapham said.
