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Breakthrough In Face Recognition Software
An anonymous reader writes: Face recognition software underwent a revolution in 2001 with the creation of the Viola-Jones algorithm. Now, the field looks set to dramatically improve once again: computer scientists from Stanford and Yahoo Labs have published a new, simple approach that can find faces turned at an angle and those that are partially blocked by something else. The researchers "capitalize on the advances made in recent years on a type of machine learning known as a deep convolutional neural network. The idea is to train a many-layered neural network using a vast database of annotated examples, in this case pictures of faces from many angles. To that end, Farfade and co created a database of 200,000 images that included faces at various angles and orientations and a further 20 million images without faces. They then trained their neural net in batches of 128 images over 50,000 iterations. ... What's more, their algorithm is significantly better at spotting faces when upside down, something other approaches haven't perfected."
"What's more, their algorithm is significantly better at spotting faces when upside down, something other approaches haven't perfected."
Add this step: Rotate the image and run the algorithm each x degrees. What am I missing?
For every "terrorist" they track through the mall, how many ordinary Joes like me who like their privacy are also tracked and stored in huge databases for all time?
Rats, there goes my ceiling-walking bank-robbery plans.
Table-ized A.I.
Very much anecdotal, but here goes anyway - a little while back, I found a recipe for cow tongue that seemed intriguing. If I had eaten it before I couldn't recall, at least I hadn't prepared it myself. So off to the butcher's I was, as this is not found in every shop. The tongues they had on display there seemed very tiny (in retrospect, they must have been veal tongues), so I said "give me the largest tongue you have". As the saying goes, "you should be careful what you wish for" - what I ended up with was a monster, something like over 1.3kg (nearly three pounds). I really didn't need that much, but all I could do was to say thanks and go home with my prey.
As I laid it on my cutting board, pretty much filling it entirely, it looked at the same time so awesome and gruesome that I had to take a photo of it (not a food blogger, or a blogger of any kind, I just had to document it). And to share the experience, I sent it to a friend via Hangouts. Now, as she uses Hangouts from the GMail web interface, the images are not visible inline but are Google+ links. So she clicks the link.
...and G+ helpfully asks her "Is this xxxxx?" (xxxxx == her name) While people are, rightfully, concerned whether companies such as Google know too much about their lives, at least when it comes to Google and facial recognition, they have a long way to go.
Debunked?
They're a machine learning algorithm. All such algorithms do is place a fancy decision boundary in a high dimensional space. DnNs do a decent job for certain classes of problem. Far away from the training data, the boundary is not useful, but that's the same with all algorithms pretty much.
So no. They haven't been debunked.
SJW n. One who posts facts.
It seems to me, as I have been following the progress of the technology over the last year or so, that it was only recently that scientists either had the idea to layer networks on top of one another, or gained the ability to. This started with the algo that would analyze pictures for content and tag them, ie a picture of a girl playing with a dog was tagged as such. It was approaching primate-level "cognition" in that specific context a few months ago, but now I have read that it has reached or surpassed peak human level, where rather than labeling the dog as a dog, it labeled it as its specific breed, or labeled a flower as its specific type that I had never heard of. Combining that with this new data point, it would seem that visual perception in machines has exploded into post-human territory. Shit is getting real.
They're using a standard technique. Convolutional networks started to become big with LeCun's 1998 paper on learning to recognize hand-written digits http://yann.lecun.com/exdb/pub... . His lenet-5 network could identify the digit accurately 99% of the time.
Convolutional networks are starting to become used to play Go, eg 'Move evaluation in Go using Deep Convolutional Neural Networks', by Maddison Huang, Sutskever and Silver, http://arxiv.org/pdf/1412.6564... Maddison et al used a 12-layer convolutional network to predict where an expect would move next with 50% accuracy :-)
Progress on convolutional networks moves forward all the time, in an incremental way. If we had one article per day about one increment it would quickly lose mass appeal though :-) The article is about one increment along the way, but does symbolize the massive progress that is being made.
Convolutional networks work well partly because they can take advantage of the massive computional capacity made available in GPU hardware.
Can I finally automatically tag the performers in my porn collection? I'm asking for a friend.
I didn't read the article, of course, but the summary sounds like they're doing face *detection* not recognition.
Detection: find which portions of an image are faces.
Recognition: compare to a database of faces and find out whose face it is.
First is way easier than the other.
I apologize for the lack of a signature.