Attractiveness Components

I was pondering the different ways people can be viewed as attractive, and decided to systematize it. I am considering only static images of a neutral appearance, so no personality cues are incoporated. Using evolutionary pressures as a guide, I came up with the following four:

• desire to protect children
• desire for general health
• desire for the gender we are sexually attracted to
• desire for peak fecundity

The first component evinces itself in what I refer to as "cuteness". It can sometimes be at odds with the last measure, depending on the one making the judgement, giving rise to the "cute but not sexy" phenomena. The second is the typical beauty measures that we hear so much about: symmetry, proportion, height, clear skin, etc. The third is simply how much your appearance contains gender cues. Note that how this is interpreted by the viewer is what matters, so it says nothing of your absolute attractiveness in its own right. A good example of someone who varies from their stereotypical gender appearance but is still very attractive would be Katharine Hepburn, who has often been referred to as a "handsome" woman. The last is straightforward, mostly being about not being too old or too young, although it can overlap the second to some extent depending on how you frame the two.

The way this could be used in practice would be to have users rate many images whose components have been determined for overall attractiveness. Standard recommendation system techniques could then be used to infer the value each user places on each component, and should then allow estimating the rating of new photos once they've had their components rated. You could omit the requirement that images have their components directly determined, inferring them as well, but that would result in far slower convergence for newly added images.

The Evolution of Lines

Good evening, ladies and gentlemen...

Bang Goes the Theory had a segment using the repeated freehand tracing of a single line to illustrate some of the basic mechanisms of evolution. It lacked any selective pressure, so I whipped up a program to add some. I started with ten perfectly straight lines, each of which had ten offspring with a small chance of containing replication errors (1% to shift each pixel one place right or left). From that pool of one hundred, I then chose the ten that most resembled my target shape and repeated the process. To keep it simple, I didn't require the line be continuous (in fact, it's not technically a line as there's only one pixel for each vertical position on the image). I used the sum of the horizontal distance squared for each pixel from it's target x value as a measure of fitness. You may recognize the final shape, which took 2,200 generations to settle upon. Its efficiency could be increased dramatically as the algorithm employed was crude.

This is a lot less involved (asexual reproduction for a start) than many genetic algorithms I've written, but it's much less abstract.