Posted on 19 January 2011

I was browsing Stack Overflow when I came across this question that asks how to use image processing and machine learning to measure the beauty of a human face. As this answer explains, papers have already been written on the topic, including this one in ECCV 2010.

At AdMIRe 2009, I remember hearing renowned researcher Malcolm Slaney say the following, paraphrased: “Computers should solve computer problems, and humans should solve human problems.” I agree 100 percent. Yet, in the exciting and sometimes amusing world of machine learning, this principle is violated all the time. The ECCV paper is just one example.

I can understand why this paper was accepted. To be fair, the paper is well written. (Having reviewed many papers, I find that proper English can go a long way.) It uses standard machine learning approaches that have been applied to similar problems such as face recognition and gender recognition, e.g., PCA, neural networks, and gradient descent. Papers that ask provocative questions and address undersaturated problems tend to be accepted, as they should. Then again, as a reviewer, I would claim that there exists no ground truth for beauty, and so the proposed system can spit out whatever nonsense it wants, and there would be no way to validate or invalidate it.

More importantly, this research is just plain mean. People are sensitive about their appearance. To propose a computerized system that can quantify and rank the beauty of human faces is provocative at best and unprofessional at worst. Oh, and all of the test subjects are female. Draw your own conclusions.

Here’s one clever piece of marketing: the authors derive two mathematical operators, beautify and beastify, where one is simply the negative of the other. (Beastify? Seriously??) Applying the beautify operator to an image makes the person more beautiful, and the beastify operator does the opposite. How cute.

The practitioner in me can never imagine such a system being used for any practical purpose. I can find utility — at least an iota of utility — in related problems such as face recognition, speaker recognition, gender recognition, genre recognition (for music), ethnicity recognition, language recognition, and gesture recognition. But where would beauty measurement be used? The Miss Universe Pageant? American Idol? Hollywood auditions? Facebook? (Actually, I wouldn’t put that last one outside the realm of possibility.)

Computers should solve computer problems. “Multiply 9832473 by 29834″ is a computer problem. “Compress this 12-megapixel image” is a computer problem. “Encode this bitstream so that it is cryptographically secure and robust to channel errors” is a computer problem. Let’s leave the human problems for humans.