
Facebook Adds Face Detection To Photos - nreece
http://www.allfacebook.com/2010/07/facebook-adds-face-detection-to-photos/
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jacksoncarter
Facebook is awesome. They have some of the most brilliant minds working there.
Pretty soon, they'll be able to tell _whose_ face it is! I can't wait until
they partner with restaurants and banks and other places with security
cameras, so I don't have to update my location or status. They can just tell
all my friends where I am and what food I ate for lunch.

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c1sc0
Facebook is awesome. They dare to state the obvious (privacy is dead) and
don't want to miss the chance of being the first to implement technology that
may seem controversial now but will seem obvious tomorrow.

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detst
Privacy isn't an issue of either having it or not. For most of us there is a
line (that moves depending on who is on the receiving end of the information)
that we don't want crossed. It's far from dead.

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nreece
While there's nothing significantly ground-breaking about it in today's time,
but Riya sort of pioneered mainstream facial recognition back in 2005
(deadpooled in August 2009) - <http://www.crunchbase.com/product/riya>

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apu
To clarify/be pedantic, what facebook just added was _face detection_ \--
detecting where the faces are in an image. _Face recognition_ usually refers
to figuring out _who_ a given face is of.

Riya was one of the first sites doing detection on images, and attempting to
do some recognition. One of the reasons they're deadpooled now is that
recognition is a really really hard problem (that's the area I do research
in). Plus, they found that it was more lucrative building product search --
hence, <http://like.com>

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anonymousDan
Hey, can I ask you a question about face recognition? So I have a similar
problem, except I want to automatically determine some characteristic of a
human from their face. However, I'm not sure how to go about determining a
relevant set of features to use. How are the features useful for face
recognition determined in state-of-the-art recognition algorithms?

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apu
Haha thanks for giving me the _perfect_ setup to mention my own research.

I've been working on automatically detecting visually describable attributes
in face images. These include everything from coarse attributes such as
gender, age, and ethnicity; to more detailed ones such as nose size, eye
shape, facial hair; and including some imaging conditions, such as blurriness,
lighting, and facial expression. (I think this is what you meant by
'characteristic', right?)

We demonstrated how to train such classifiers and use them for building a face
image search engine in this project:

<http://www.cs.columbia.edu/CAVE/projects/facesearch/>

We demonstrated how to use these attributes to perform face verification ("are
these two images of the same person?") in this project:

<http://www.cs.columbia.edu/CAVE/projects/faceverification/>

Those project pages have descriptions of how everything works, and also links
to the actual publications themselves. We've also released two databases that
might be useful for training your own classifiers:

<http://www.cs.columbia.edu/CAVE/databases/facetracer/>

<http://www.cs.columbia.edu/CAVE/databases/pubfig/>

Now to actually answer your question: we found that the key to training
different attribute classifiers is to use different features for each one. The
"secret sauce" of our work is a feature selection algorithm that looks at a
large pool of possible features (divided into regions of the face to extract
features from, what type of features to extract, how to normalize the feature
vector, and how to aggregate the normalized values) and picks the most
appropriate ones for a given attribute.

Most existing face recognition algorithms typically choose a few low-level
features (often things related to image gradients, since this gets rid of
lighting variations) and use these to compare face images. However, works such
as ours are trying to change this by looking at higher-level attributes in
addition to just the low-level features. The big reason for this is that low-
level features require very precise alignment between pairs of faces to work
well -- a precision not reachable on real world images. Luckily, the level of
alignment possible with today's methods is good enough to accurately train our
high-level attribute classifiers, and so they end up being more useful for
recognition in real world datasets.

Finally, one of the best benchmarks for performance on real-world face
recognition is "Labeled Faces in the Wild" (LFW):

<http://vis-www.cs.umass.edu/lfw/>

That has results showing the performance of the best current approaches as
well as links to papers. It's also a nice dataset of images to test on.

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gkoberger
It doesn't seem like it would be too hard for Facebook to implement facial
recognition. It's clearly not a trivial issue, however they certainly have the
data, servers and intelligence to do it right. After all, they have a list of
everyone you come in contact with (as well as friends of friends), along with
anywhere between a dozen and a thousand tagged pictures of each.

EDIT: In reply to the poster below me- of course, autotagging wouldn't work. I
was thinking more of a "suggestions" implementation.

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adi92
it seems like to much of a risk, in terms of the controversies that might get
raised every time the recognition software screws up.. imagine the system
tagging somebody's ex girlfriend on their current partner's face, or
interpreting a picture of Saddam as some random friend of the poster

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schn
If you read the article,

 _"...the system will automatically detect faces in the photos and prompt you
to select the friend who’s face it is."_

It seems that the software is (so far) only recognising the presence of a
face, not identity.

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petervandijck
Original post here: <http://blog.facebook.com/blog.php?post=403838582130>

