A while back we built the website FaceStat, where you can upload a picture of yourself and find out what kind of first impression you would make to a stranger on the internet, and also judge others in kind.
To date, we’ve collected more than ten million judgments on over one hundred thousand faces. On a lazy Saturday afternoon, we finally dumped the data and played around with it.
Aggregating millions of these snap decisions tells us a lot about our own biases in surprising ways.
For example, you might think that 20-year-olds would be judged as most attractive. However, in this data babies are most attractive, with another peak around 26. After a dip from 40-50, attractiveness starts to increase again.
We have far more data on people between 18-40 on our website, which explains the tighter error bars.
Women are judged as much more trustworthy than men, with the lowest scores for adolescent males. Interestingly, there is a large jump in trustworthiness for both men and women between 20 and 30, and between 50 and 60:
Children and old people are judged as more intelligent, with males in their twenties getting the lowest scores.
As men and women get older they are thought to be more and more conservative. It’s interesting that young women are perceived as more liberal than young men, but the gap disappears after 25.
A few more details on the above.
- FaceStat has more female than male users. Users both upload faces and also judge others. Judgments were collected over the last eight months. All faces have at least 100 judgments each.
- We grouped faces by perceived age, one bin per year, and plotted one point for the average value of the attribute. The y-axis is normalized: centered on the average face rating, with tick marks for +/- 1 standard deviation across faces.
- Error bars are 95% confidence intervals, though omitted on small sized bins where they would be extremely large. In some sense they are too large (too conservative), since each age year is treated separately. The line is a loess fit.
Finally, here’s a scatterplot matrix of the attributes. Every pair of attributes has two graph panels. The bottom-left panels are smoothed scatterplots that show the density of faces in that attribute pair’s space. The top-right corrgram panels show Pearson correlations: blue means the two attributes are positively correlated, and red means negatively correlated. Unlike the above graphs, genders are not separated, and values are not normalized.
There’s a story in each panel. Looking at the attributes in the middle, we see that conservativeness, wealth, intelligence, and trustworthiness all seem to go together. Intoxication has lots of red panels: it’s anticorrelated with all of them. Age would go along being correlated with all these things, except that extreme youth gets high intelligence and trustworthiness marks. Attractiveness is more complex too: it sometimes goes down at the extremes. Perceived political moderates look more attractive compared to liberals and conservatives; similarly, you’re hot if you look moderately smart or rich, but hideously high wealth and intelligence are a little less attractive.
(On the age-attractiveness scatterplot, note the “old beautiful people” effect seems to weaken compared to the gender breakdown graphs earlier in this post. Simpson’s Paradox?)