So, a lot of my friends have been asking why I don’t hang out with anyone anymore. While this isn’t entirely true (I went to Mt.Mogan just last week with a bunch of people for instance), I did spend my time wisely (or unwisely depending on your perspective). I’ve been interested in the concept of so-called “universal beauty standard” for a while now, or over a decade to be more accurately. Personally, I don’t think the beauty standard is entirely universal yet I did find the features that stand out as attractive to be similar if not identical across many cultures. This is why I decided to use deep learning neural network to create an AI that feeds on all users’ interactions (altho to be fair now I’ve only built an App based on wechat’s platform as the entry point since it would be the most effectively way to get users, at least in Asia, to use my app), and eventually, hopefully, become a super AI that represents the universal beauty standard, if there is one.

Let me first introduce how this app works, since it is in Chinese, it might be pretty hard to understand for most people worldwide. What you see below is the main interface of the app, I thought about introducing an engagement score system to keep people interested in participating, so far it looks like people want to participate regardless so I haven’t really implement anything meaningful with the scoring system just yet. After all, this is a work-in-progress.

Well, to get to see these many facefights in your console, first you obviously need to upload selfies (or photos of other people, haha). To be honest, the selfie tab was such a UX nightmare at first even Chinese users are confused. A lot of users failed to find the submit button because it often requires to scroll down to see it — if you have one of the devices that have a smaller screen. Alone with the invocation chain issue (since at the server end, it needs to do multiple actions at once, and particularly a neural network could take a while to run, when the query is large, it could easily throw an error), users often just gave up right here. I guess my excuse for not fixing the UI (making everything fit in one screen for example) yet is that I was working on bigger problems such as optimizing the call chain or improving the efficiency of the neural network.

Once your selfie is submitted, you will enter the facefight. But you still need to find someone to fight with, right? The idea here is to encourage users to participate and having them to encourage their friends and family to participate too. The method proves to be working for mostly younger men and women (especially between age of 25 to 30) but most people from the age group above 35 don’t seem to want to let other people see his or her attractiveness score.

This is what your friend would see when they open your facefight invite. It pretty much is the same like the selfie tab, except your selfie will be on the top left corner.

Next we have the most important part of the app, lol, the facefight itself. What I found interesting is that most people choose to upload their real selfies at first, but after the introduction of leaderboard, more and more users started to post photos of other people (or a lot of celebrities decided to start using my app). This is a social engineering I yet to solve, considering without a leaderboard, users tend to forget about the app after the first entry.

That’s pretty much how the app works. I will leave leaderboard and favorites for you to explore. But you could be confused at this moment. Oh yes, wechat. So wechat is not a new operating system. It is just a chatting app, at least that is what it seems from the outside. You can find it in the playstore or app store very easily. Or you can checkout their website here: http://www.wechat.com/en/

What’s amazing about wechat is that it is also an app platform, meaning apps run on wechat. This means, much like facebook, you can get wechat’s users without having them to sign up first. Using wechat’s platform has been proven effective for facefight. Normally you expect a couple of hundreds of users in the first month, but facefight got around 140K users in its first month.

Additionally, you don’t have to look for facefight (which you can by typing its Chinese name 脸战) in wechat’s app drawer, just scan this QR code with your wechat’s camera feature, it will open right up. :)

P.S., this is a merit based training system, meaning it rewards people from getting votes, and once a while the ceiling of the max score would go up, that’s when the overall score will decline. So if a lot of people are voting and there is no universal beauty standard, everyone will eventually be getting scores near 50. So far I don’t see any race, gender, or age group stand out scoring higher than others, except Asian females from 25 to 30, because most photos in the system are asian females, and they are getting tons of the votes.

The entire system is rather complicated, but in the score is not independent from the age, gender, emotions, and other attributes. Same photo would be rated differently if it is in a different age group for example (or gender group…x group). Of course that’s not exactly what happens. I think it might take a while to explain this. So I will pass for now. There is also “risk management” feature in place, meaning a different AI will double check invalid entries before the retraining happens. Kinda like a parent telling the kid, oh no no, this is pornography so even if it is getting a lot of votes, it is not going into the retraining dataset. Also there are a lot of “sudden stop” features built in, like it crops out all unnecessary pixel other than faces, so let’s say a few photos are getting lots of votes because the photos demonstrate a significant portion of abs or boobs, lol, the neural network is not seeing these information (unless of course there is a connection between your facial features to wether your photos shows abs, my common sense says no).

In terms of the similarity between facefight and facemash (especially originally I thought of facematch as the name), I guess subconsciously I did see the connection, but the mechanism was really from one of my alums at CMU. This is still a work-in-progress, so I am going to add new components and experience into the app. Stay tuned.