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Face Recognition Algorithm Finally Outperforms Humans
KentuckyFC (1144503) writes "Face recognition has come a long way in recent years. In ideal lighting conditions, given the same pose, facial expression etc, it easily outperforms humans. But the real world isn't like that. People grow beards, wear make up and glasses, make strange faces and so on, which makes the task of facial recognition tricky even for humans. A well-known photo database called Labelled Faces in the Wild captures much of this variation. It consists of 13,000 face images of almost 6000 public figures collected off the web. When images of the same person are paired, humans can correctly spot matches and mismatches 97.53 per cent of the time. By comparison, face recognition algorithms have never come close to this. Now a group of computer scientists have developed a new algorithm called GaussianFace that outperforms humans in this task for the first time. The algorithm normalises each face into a 150 x 120 pixel image by transforming it based on five image landmarks: the position of both eyes, the nose and the two corners of the mouth. After being trained on a wide variety of images in advance, it can then compare faces looking for similarities. It does this with an accuracy of 98.52 per cent; the first time an algorithm has beaten human-level performance in such challenging real-world conditions. You can test yourself on some of the image pairs on the other side of the link."
Of all the singularity-ish technological advances I could be alive for, it had to be this one :-(
"When information is power, privacy is freedom" - Jah-Wren Ryel
"Are you, or are you not a member of the Marxist Party!?"
"Please restate the question, I'll only answer one at a time!" *wiggles eyebrows*
"When information is power, privacy is freedom" - Jah-Wren Ryel
But Human's don't match faces based on the entire population either. Just on the faces they know.
I don't know how many people the average person can recognise, but my guess is that it will be less than 13,000.
This anthropologist seems to have worked in this area, and he puts the number of people you can recognise and put a name to as 1500. (You'll recognise more than that, but you won't have names to go with them.)
http://spectrum.ieee.org/telec...
For sure, disguises to baffle algorithms differ from disguises to baffle humans. Here's a web site about disguises to baffle facial recognition systems. Probably not something anyone would want to wear outside a fashion event or a political demo, but interesting anyway.
http://cvdazzle.com/
Both the title and description are sensationalism. If you read the paper, the algorithm works at 98.52 on 1 of 5 data sets. We could also accurately say the algorithm is only 89.33% accurate. The score from the most difficult database. Much worse than the normal person.
That being said, it is much better than other publicly available algorithms.
Microsoft, Apple, Google, Amazon what's the difference? All steal money from devs and control with walled gardens.
The other stunning form of Lord Shiva is His half-female version . If you could manage this form, you would discombobulate not just Gaussian Face but also fellow humans too.
Extending the theory, painting random noses, lips, eyes and other features on cheeks, foreheads etc would defeat these automatic face recognition systems.
sed -e 's/Chuck Norris/Rajnikant/g' joke > fact
and/or sensationalism... a 13000 image set a whole population does not make. It's probably just better than the existing algorithms somewhat.
I doubt my internal facial recognition distinguishes between 13000 people. And worse, as damning as it sounds, it really starts to fail when dealing with people of certain races that i just didn't get much exposure to as a kid.