Your Face Alone Can Reveal Your Biological Age to a Computer

By Jamie Condliffe on at

If anyone's ever gazed into your eyes and told you that you look old, they might soon have an algorithm that agrees with them. A new study reveals that your face alone can be used to predict your biological age computationally, with a high degree of accuracy.

Using 3D scans of people's faces, a team from the Shanghai Institutes of Biological Sciences in China can identify facial features that change dramatically with age, such as the smoothness of skin and distance between mouth and nose. Using scans of 332 Chinese volunteers aged between 17 and 77, they've created a composite which can be used as a yardstick to estimate a new scan's biological age — rather than their actual, temporal age.

To check their results, they compared the predicted ages of the computer algorithm with bio-markers in blood samples, that reveal biological ageing. While the algorithm sometimes predicts participants are as much as six years older than their real age, bio-markers also reveal that they are biologically older – such as exhibiting signs of higher cholesterol level than one would expect. Underestimates were equally accounted for in this way.

The technology could provide a means of estimating biological — rather than temporal — age quickly and easily. Indeed, the aim is to use the algorithm to create an app that can be used by doctors to quickly estimate biological age, reports New Scientist. In turn, those who appear to be ageing faster than expected could be further examined and tested to establish why—be it lack of exercise, diet or something more serious.

It's not, of course, the ultimate test. After all, ageing is currently thought to be caused by the gradual degradation of the tips of our chromosomes; no 3D image can take that into account. But as a means of diagnosing those at risk, the technology could yet prove and efficient tool in keeping the world at least a little healthier. [Cell Research via New Scientist]

Image by Chen et al. Shanghai Institutes of Biological Sciences