From: Megan Molteni via Wired
Published February 23, 2017 09:39 AM

What, You Can't Tell Two Lemurs Apart? Computers Can

The Centre Valbio research station, a modern building of stone and glass set in the jungled hills at the edge of Madagascar’s Ranomafana National Park, was starting to look like the third season of The Wire. Big tackboards lined the walls, each one covered with dozens of pinned-up photographs. Some images were grouped together in families, while others floated alone, unconnected. It was 2012, and Rachel Jacobs was using Detective McNulty-style tactics to sort out the associations in a very different kind of crew: the park’s population of red-bellied lemurs.

A biological anthropologist, Jacobs was studying how color vision evolved in lemurs, which meant keeping track of more than 100 animals. She got good at telling them apart. After Jacobs finished her dissertation, her Ranomafana colleagues kept calling her up for lemur ID help—so much that the Skype pings got overwhelming. So Jacobs started sending emails to every computer vision expert she could find. Last week, after years of working with students and faculty at Michigan State University to train an artificial deep neural network on her stash of field photos, Jacobs finally revealed her second set of eyes: LemurFaceID.

The program is a facial recognition system much like the ones Facebook and Google use for people. But instead of looking at facial geometries—like the distance between your eyes, or the length of your nose—LemurFaceID uses 10×10-pixel squares to identify differences in fur texture. (Also like human face recognition software, photos have to be black and white for LemurFaceID to work.) It’s good enough to correctly identify a lemur out of a known set of individuals 98.7 percent of the time.

Read more at Wired

Photo credit: © via Wikimedia Commons

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