Computers are able to use monkey facial patterns not only to correctly identify species, but also distinguish individuals within species, a team of scientists has found. Their findings, which rely on computer algorithms to identify guenon monkeys, suggest that machine learning can be a tool in studying evolution and help to identify the factors that have led to facial differentiation in monkey evolution.
“Studying the cues that species use to discriminate each other often poses a challenge to scientists,” explains James Higham, an assistant professor of anthropology at New York University and one of authors of the study, which appears in the journal “Proceedings of the Royal Society B”. “Many species are now rare and, in the case of these particular monkeys, they live high in the rainforest canopy, so are very difficult to reach.”
The analysis focused on specific guenon visual signals—facial patterns generally as described using the ‘eigenface’ technique, a method used in computer vision for human facial recognition, as well as eyebrow patches and nose spots segmented from images. From here, the researchers tested whether or not an algorithm could accurately accomplish the following: identify individual guenons, classify them by species from among the 12 in the sample, and determine the age and sex of each individual.