Since we launched FaceStat we’ve collected over 100,000 images of people with different types of labels. Explore the faces and labels with our new interface:

Direct link to the interface

(Go to facestat.com and help us collect more data )

Before last weekend, our FaceStat website was chugging away with a small but loyal userbase:

But on Sunday, an insane number of people suddenly decided to flock to our site. Let’s extend the previous chart by 2 days, then a little bit of y-axis auto-scaling says it all:

Turns out the giant spike was due to our being featured via a news article on Yahoo.com’s front page!

Of course, we had to frantically rearchitect the system and scale it under this deluge of traffic. You can read the blow-by-blow account of our crazy few days on Lukas’s blog, here.

The web startup community seems pretty interested in the mad scaling issues, so I’ll respond to some of the comments on Lukas’s blog below:

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As the photos and judgments are stacking up at FaceStat, I thought it would be interesting to see relationships between tags – when tag X appears very frequently with tag Y. I downloaded more than 4,000 rows of tags, where each row corresponds to one face, and ran a Python script to count concurrences.

Some interesting trends (where the first word often occurs with the others):

• Old: dad, wise, jolly, pedophile, grandpa, perv, professor, writer, sour, experienced, dead, matronly, alcoholic
• Smart: business, librarian, azn, graduate, genius, engineer, intent, bookworm
• Oily: wet, sweaty, shiny
• Drunk: drunkard, tipsy, alcoholic, partyboy, scene, stripper, wasted, ditzy, ew
• Young: tween, underage, uninterested, teen, childlike, jailbait, child, tooyoung, highschooler, babyface, kid, boy, innocent, virgin
• Athletic: runner, driven, jock, sporty
• Serious: angry, grumpy, direct, piercing, alert, dedicated, azn, doctor, suave
• Nerd: goodlooking, virgin, slacker, nerdy, goof, geek
• Gay: cowboy, flamboyant, metrosexual, yuppie, feminine, queer, homosexual, out, pissed, dangerous

Other highly concurrent pairs: army & dedicated, fighter & patriotic, skeezy & hairy, mustache & dad, sunglasses & secretive, cougar & milf, naked & creepy, pimp & playa, plastic & fake, tease & sexy, badass & cool.

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In 2007, researchers at Aberdeen University’s Face Research Laboratory showed that women found the face of a man more attractive when the face of a woman was smiling at it. (NewScientist) Biologists have a term, “mate choice copying”, for similar behavior in birds.

I was looking at some photos of couples on FaceStat and wanted to run a quick and dirty version of this experiment, and including the other FaceStat variables. I photoshopped out the girl from guy/girl photos and reuploaded. The results followed the same trend as the Aberdeen study (although the Turkers are mixed gender). The strongest, most consistent difference for the photoshopped photo was in relationship status (more single, obviously) and in attractiveness (25% of people found them less attractive). Some examples:

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If you could ask dozens of random people on the internet questions like “Do I look smart?” or “Does this dress make me look fat?”, why wouldn’t you? Today there are no excuses! With the launch of FaceStat you have the power to decide exactly what profile picture is right for the 10 different social networks you’re a part of. You have the power to make sure the photo you’re about to upload to Match.com makes you look attractive and funny. You have the power to make sure you look trustworthy and never divorced before your girlfriend sends that picture to her parents.

A couple weeks ago we took some pictures and asked Turkers a bunch of questions. We thought this was really cool so we built a tool to let anybody see what people had to say about their picture.

You just upload a picture from your hard drive or Facebook, and we send the faces out to Mechanical Turk to be judged. Currently you can get statistics on one photo per day. Browse the latest faces, or even see who looks the most married.

Add some faces, and see how you stack up!

-Chris

In the previous post I talked about how we scraped about one hundred photos from social networking sites and had Turkers guess those people’s traits – including age, race, intelligence, political affiliation, and intoxication.

Intoxication was listed as a checkbox. Averaging the guesses for each photo gives us an intoxication rating from 0 (sober) to 1 (smashed). In the histogram, you’ll notice the familiar power law distribution of drunkenness:

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How do photos uploaded to social networking sites reflect back on you? We scraped the profiles of about one hundred people on a social network and had Turkers guess those people’s traits – including age, ethnicity, intelligence, political affiliation, and intoxication. To be fair, we included pictures of ourselves in the batch.

You can see the photos ordered on three axes here:

More details after the jump:

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