
A Look Into Facebook's Potential to Recognize Anybody's Face - dkoch
http://www.npr.org/blogs/alltechconsidered/2013/10/28/228181778/a-look-into-facebooks-potential-to-recognize-anybodys-face
======
jcutrell
I heard from an unnamed Google employee once about very similar
responsibility-laden abilities with the massive number of images Google has
access too.

In particular, a conversation about recognition algorithms for sorting
pornography at the age-level was brought up. Now, I know this has been covered
on here before, but what a massive amount of incriminating information must be
kept in those servers.

I really would like to be able to have access to facial model information for
my hypothetical advertising company. Can you imagine if I could, for instance,
find out what primary five face shapes people in Iowa find trustworthy, with a
high (enough) level of accuracy? I'd pay a whole lot for that, hypothetically.

------
speedyrev
Not only do they have face models. But they have immense details about those
faces. Location, relationship connections, product endorsements, work and
school connections... After the last few revelations about the NSA that have
come out. I think you should assume that it is all being used.

------
Irishsteve
If each Facebook user has at least 100 photos they are tagged in under a
variety of scenarios, quality etc. I'd of thought that would be a pretty
reliable way of training some vision algo.

Seems thats not the case.

~~~
apu
It would, perhaps, but the problem is deciding between millions (or billions)
of people -- if you recognize people by doing pair-wise comparisons, then even
small error rates can quickly add up.

~~~
seiji
All you have to do is turn people into a feature vector then KNN them. Perhaps
once you're in a smaller neighborhood, you can do more intensive analysis for
high probability equality matching.

~~~
apu
This is not going to work (at least currently). Features for faces are not yet
powerful enough to get you to a reasonable set of matches without also missing
the right face in a large percentage of cases. Variations due to lighting,
pose, and expression end up causing the same person to look very different --
often to the point where two different people in the same configuration can
look more similar than the same person in different configurations.

That being said, if the database is small enough (e.g., in a limited scenario
or by applying other non-vision filters first), then the state of the art
methods do use a similar approach. However, in practice people use methods
with much stronger priors than KNN. Because faces fall into a low-dimensional
manifold[1], you can take advantage of that to constrain queries much more
than with generic KNN.

[1] My PhD advisor has published several works about this topic, e.g.,
[http://vision.ucsd.edu/kriegman-
grp/papers/ijcv98.pdf](http://vision.ucsd.edu/kriegman-grp/papers/ijcv98.pdf)
[http://virtualhost.cs.columbia.edu/~belhumeur/journal/invari...](http://virtualhost.cs.columbia.edu/~belhumeur/journal/invariance_v2.pdf)
[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.123...](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.123.3344&rep=rep1&type=pdf)

------
hnriot
This, I suspect, is why In-Q-Tel (CIA) was an original investor in FB.

------
amirmc
This reminded me a of a TED talk I saw recently:
[https://www.youtube.com/watch?v=H_pqhMO3ZSY](https://www.youtube.com/watch?v=H_pqhMO3ZSY)

It's only 15mins long but you can skip to 2:35 for one of the face experiments
and to 8:00 for an experiment they're running now. That latter experiment is
quite creepy but I'd love to know the results.

------
phyalow
I'm not surprised given the companies name...

------
BIair
I think it would be incredibly cool just to see your doppelgangers. How many
people look like you, and how similar they are. Similar to searching for your
name, only to discover other people have the same name, and the curiosity of
who they are. Imagine searching for people who have the same face?

