Don’t you hate it when you lose your Lemur in a crowd? We jest, but this is a very real problem for biologists. So now, there’s an app for that.
A team of lemur biologists and computer scientists have modified human facial recognition methods to develop a semi-automated system that can identify individual lemurs. The new technology is called “LemurFaceID”.
According to the research team from The George Washington University, University of Arizona, Hunter College, and Michigan State University in the USA, this is the first time that facial recognition technology has been applied to any of the over 100 lemur species endemic to Madagascar.
The researchers showed that LemurFaceID can correctly identify individual lemurs with 98.7 per cent accuracy, given two face images of the individual.
“Using photos we had taken of wild red-bellied lemurs in Ranomafana National Park, Madagascar, researcher Anil Jain and members from his laboratory were able to adapt a facial recognition system designed for human faces so that it recognises individual lemurs based on their facial characteristics,” explained Dr Rachel Jacobs from The George Washington University.
“We were surprised with the high degree of accuracy that we achieved, which shows that facial recognition can be a useful tool for lemur identification.”
For short-term studies of lemurs, researchers often rely on unique, individual identifiers to recognise individual lemurs, such as differences in body size and shape or the presence of injuries and scars. However, relying on variations in appearance can make it difficult for different researchers to identify the same individual over time. This and other factors mean that long-term, multi-generation studies of lemur populations are limited.
“Studying individuals and populations over long periods of time provides crucial data on how long individuals live in the wild, how frequently they reproduce, as well as rates of infant and juvenile mortality and ultimately population growth and decline,” said Stacey Tecot, senior researcher.
“Information like that can inform conservation strategies for lemurs, a highly endangered group of mammals.”
The researchers suggest that the new technology could remove many of the limitations associated with traditional methods for lemur identification.
“Capture and collar methods are a common practice for the identification of wild lemurs but these methods can pose risks to the animals, such as injury or stress, as well as costs for veterinary services and anesthesia,” Dr Jacobs explained. “Our method is non-invasive and would help reduce or eliminate some of these costs.”
To address the challenge of developing a non-invasive method for identifying individual lemurs that can facilitate long-term research, the researchers modified and tested human facial recognition technology specifically for lemur faces, using a dataset of 462 images of 80 red-bellied lemur individuals, and a database containing a further 190 images of other lemur species.
Many lemur faces possess unique features such as hair and skin patterns that computer systems can be trained to recognise.
In addition to expanding research on lemur populations and assisting conservation efforts, the researchers believe that the face recognition methods developed for LemurFaceID could be useful for identification of other primate and non-primate species with variable facial hair and skin patterns, such as bears, red pandas, raccoons or sloths.
The authors also point out that in non-captive settings, where unknown individuals might enter a population, the system’s accuracy was lower and further testing involving larger datasets of individuals and photographs is needed.