Many people worry that face recognition is intrusive, but wild animals seem to find it bearable.
What’s new: Melanie Clapham at University of Victoria with teammates of the BearID Project developed a model that performs face recognition for brown bears.
How it works: BearID recognizes individual bears with 84 percent accuracy. It comprises four components: bearface, bearchip, bearembed, and bearsvm.
- Bearface detects bear faces. It’s a variation on Dog Hipsterizer, an application that whimsically decorates pictures of pooches with eye glasses and mustaches, trained and tested on 4,675 photos of 132 bears.
- Bearchip reorients and crops the image.
- Bearembed generates a representation of the face. It’s a ResNet-34 adapted from the Dlib library. The authors trained it on cropped images from the training set to make features of the same bear similar and features of different bears dissimilar.
- Bearsvm, also adapted from Dlib, labels the representation as an individual. It’s a linear SVM trained using features generated by Bearembed and ID labels in the training set.
Behind the news: Face recognition systems have been built for a growing number of non-human species, including chimpanzees, lemurs, and pandas.
Why it matters: By providing a low-cost way to track individual animals, apps like BearID could help researchers and conservationists map habitats for protection and monitor the health of animal populations. Clapham has been experimenting with the model in the field, and the team hopes to pair it with camera traps, which would allow researchers to monitor large wild populations.
We’re thinking: We’re so impressed, we can bearly contain our appaws!