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Computers Outperform Humans at Recognizing Faces
seven of five writes "According to the recent Face Recognition Grand Challenge, The match up of face-recognition algorithms showed that machine recognition of human individuals has improved tenfold since 2002 and a hundredfold since 1995. 'Among other advantages, 3-D facial recognition identifies individuals by exploiting distinctive features of a human face's surface--for instance, the curves of the eye sockets, nose, and chin, which are where tissue and bone are most apparent and which don't change over time. Furthermore, Phillips says, "changes in illumination have adversely affected face-recognition performance from still images. But the shape of a face isn't affected by changes in illumination." Hence, 3-D face recognition might even be used in near-dark conditions.'"
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It's really annoying how much of this research never gets turned into product.. or, worse yet, it gets embedded in some proprietary piece of shit hardware instead of being released as a reusable component. I'd love to add some good facial recognition to my pet robot, but I'm not buying your watt sucking camera.
How we know is more important than what we know.
I wonder whether these scientists lose any sleep over how their research advances will contribute to the future of our societies.
When a human makes a mistake recognizing a face, they suffer the results. If that's identifying a criminal, they can be cross-examined, or even sued or jailed, depending on what they said that face did.
When computers mis-ID a face, do we cross-examine and maybe punish its programmers?
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Screw the criminals. Thinks about how many law abiding citizens you'll be able to track. :D
"for instance, the curves of the eye sockets, nose, and chin, which are where tissue and bone are most apparent and which don't change over time."
Geez.
How we know is more important than what we know.
It's a question of time 'till there 's a law that forbids to wear anything that partially covers your face in certain public areas.
I think I have about ten years 'till computers are able to interpret my front-head as a 'face' so I'm safe.
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Human face recognition is run by a several hardwired circuits operating in parallel (ie. fast, with little control) with the results put together after by some heuristics -- a good enough guess. What humans need to get from facial recognition, and what their ancestors required and so developed through evolution, is nowhere near the same thing facial recognition software is after. Humans need to recognize quickly that there is a face and what information it's displaying far more than they need to differentiate one from another. Facial recognition software does just the opposite. Also, the software does the complete job every time. Humans only process as much as they need to in any given instance.
If "better" is based on the standards of humans (fastest good enough guess) rather than machines (as correct as possible, complete & in depth), humans win.
"I may be synthetic, but I'm not stupid." -- Bishop 341-B
How good are computers at recognizing a face after ten or twenty years?
Hopefully better than I am, otherwise they better be damn good at making generic bullshit small talk at family parties while sweating profusely and fishing for hints without letting on they have absolutely no idea who it is that just ambused them at the buffet table.
Actually, the opposite situation is just as bad. I have enough of a problem with my 'aunt's old room-mate' or equivilant telling me they "remeber me when I was just 'this' big" (given the amount of random old women that at some point 'changed my diaper' I have begun to wonder if my parents rented me out as a training aid), I do not need the computer hardware in my life pulling the same act!
Either way.... this will end badly.
A few years back (well, nearly a decade actually), I did my master's thesis in a lab that among other things did work on face recognition. The experts there assured me that perhaps in 50 years or so computers might be able to approach human face recognition capabilities. Apparently the development was far quicker than they could have imagined.
An interesting technical point is that in fact the algorithms haven't changed a lot since then - it's still mainly various adaptive systems such as neural networks and support vector machines. The really big breakthrough is in the data collection - in the sensors and scanners. What they couldn't imagine a decade ago was the type of accurate automatic 3d face modeling and measurements that can be done today. It's also how certain computing methods that were deemed unsuitable a few years ago are coming back big time (neural nets for instance). I guess the time wasn't ready for them the last time due to computing power and memory limitations (and of course sensors as in this case).
After LA the incident in May where protesters and cameramen kept running into police batons and shooting themselves with stolen police guns, the LAPD wants the city council to ban masks and goggles from public demonstrations. A law somewhere in Europe against masks was recently applied to burkas (no source, but google can backup any claim).
Right.
Recognition tasks are almost all inductive in nature, where performance on math is deductive. Human induction pretty well spanks machine induction at most of the things we take for granted - like recognizing and decoding faces, voices, speech, the sound of your walk, etc., etc., etc. The thing computers do least well is infer what bits of information are most important. We seem to excel at that.
Despite what the findings say, I stand by the faces thing. It sounds like the recognition algorithms got high-resolution 3D scans of human faces as input. Wake me when they can do as well as a human with low-resolution 2D scans.
That being said, it's great to see progress in this area. I can't wait until someone has to lop off my head and carry it with them in a plastic bag in order to break into my workplace. It's more grisly than taking a thumb, but much less likely to happen... I think...
I got my Linux laptop at System76.
Go is not yet as well-automated as chess, but it appears that go-playing software is rapidly advancing:
t ml
"Two Hungarian scientists have now come up with an algorithm that helps computers pick the right move in Go, played by millions around the world, in which players must capture spaces by placing black and white marbles on a board in turn.
"On a nine by nine board we are not far from reaching the level of a professional Go player," said Levente Kocsis at the Hungarian Academy of Sciences' computing lab SZTAKI.
The 19 by 19 board which top players use is still hard for a machine, but the new method is promising because it makes better use of the growing power of computers than earlier Go software."
Link
See also:
http://zaphod.aml.sztaki.hu/papers/ecml06.pdf
http://citeseer.ist.psu.edu/vanderwerf03solving.h
http://www.primidi.com/2007/02/26.html
-kgj
-kgj
I'll be impressed when they can recognize caricatures as well as humans.
One of the academic research areas I've been involved in, is study of the so-called "Other Race Effect". There is some evidence that people have quantifiable error when asked to identify faces of people of other races than their own.
Computers won't be subject to this.
-fb Everything not expressly forbidden is now mandatory.
The problem becomes how do you "flash" an image at a computer. A computer has a perfect memory. So you can't compare humans and computers in this way. A computer could completely memorize millions of faces, or even all the faces in the world, given enough storage space. 6 Billion people x 1 MB (exaggeration) per picture is only 6 petabytes. It's a lot of data, but not out of reach. So if computers get good enough at recognizing faces, it could become a useful too in security. Think about the security guard sitting at the front desk of 20 story building. Do you think he could identify every person who walks through those doors. Would he know if you were just using a stolen security badge?
Anthropic principle: We see the universe the way it is because if it were different we would not be here to see it.