Data-Driven Enhancement of Facial Attractiveness

The PDF branches off of Active Shape Modeling (by Tim Cootes, <a href="https://secure.wikimedia.org/wikipedia/en/wiki/Active_shape_model" rel="nofollow">https://secure.wikimedia.org/wikipedia/en/wiki/Active_shape_...</a>) with a style I'm not entirely familiar with, however basing off my background I think I can expain a little of what is going one.<p>Active Appearance Models are a extension of Active Shape Modeling, effectively taking a set of landmark points on a shape (in this case a face), and averaging them out to the 'average shape' of the face. AAMs take it to the next level by taking this average face and warping the original landmarked face to the average shape. From here it takes the average of the textures of the face, producing eigenvectors. These vectors would be used as a unique identifier if the program was running some form of face recognition.<p>In lieu of face recognition something you can use the averaged AAM face for it recreating a face BASED off the average (say for this paper's example: beauty). By generating an averaged AAM model off only those subjects that scored an 8 (out of 10) or above, you create the 'average attractive face'.<p>Now, if you take John Doe's face with a score 5, and generate the eigenvectors that would represent his face based on the 'Good-Looking People Only Scale', it would create a better looking version of him.<p>Another application of this technique is digital face aging (example here: <a href="http://www.intechopen.com/source/pdfs/14645/InTech-Implications_of_adult_facial_aging_on_biometrics.pdf" rel="nofollow">http://www.intechopen.com/source/pdfs/14645/InTech-Implicati...</a>) in which instead of 'morphing' the subject's face to look more attractive, you morph the subject's to look 'older' based on statistical averages based on age range, gender, and ethnicity.

There are 100,000 people on online dating sites that would pay $20 each for this.

What do you guys think about a smartphone app that could be panned around to capture a 3D image then used the golden mask to calculate an objective attractiveness score?<p>Would be some fun engineering and probably pretty popular.

Why do they never release the applications for these things? Copyright?

Forget fatbooth. Make this an iPhone app and you would be a millionaire!

Most common attractive feature: a smile! Even if not outright happy, just looking relaxed or cheerful helps.

Is the software pictured at the bottom of the page avail to download?

As far as I can see from the pictures, people like women more when they smile, and men more when they lose weight.

For the male example at the top, the shape of the face is so changed it doesn't look like the same person.

Man, I'd hate to be one of the models for one of these pictures. "Ohhhh yeah," say thousands of internet users. "That computer-generated version really <i>is</i> a lot better-looking than the real one due to those horrible flaws on that person's face". No thanks, I'd rather not see the difference between my face and a better-looking version of my face, it'd just make me feel bad.<p>On a broader point though, the human taste for "average-looking" faces is an interesting one. Aside from anything else it's an interesting Nash equilibrium -- I want to find a mate with an average-looking face so that we can produce children with average-looking faces who will be the most desirable breeding partners in the next generation.<p>What I really wonder is whether "average-looking" is hard-wired in, or whether we're programmed to spend our childhood scanning all the faces around us and mentally averaging them out to determine what a human face <i>should</i> look like. I suspect the latter -- it's a much more stable strategy over evolutionary timescales, and also explains things like why people often find people of their own race more attractive, and why mixed-race people are often unusually attractive.

You know what i think, the filters put a smile on these photos rather than anything else.

It appears to just make each face smile more.