Information about this one is still tricking in, so take it with a grain of salt, but security company [Bkav] is claiming they have defeated the Face ID system featured in Apple’s iPhone X. By combining 2D images and 3D scans of the owner’s face, [Bkav] has come up with a rather nightmarish creation that apparently fools the iPhone into believing it’s the actual owner. Few details have been released so far, but a YouTube video recently uploaded by the company does look fairly convincing.
For those who may not be keeping up with this sort of thing, Face ID is advertised as an improvement over previous face-matching identification systems (like the one baked into Android) by using two cameras and a projected IR pattern to perform a fast 3D scan of the face looking at the screen. Incidentally, this is very similar to how Microsoft’s Kinect works. While a 2D system can be fooled by a high quality photograph, a 3D based system would reject it as the face would have no depth.
[Bkav] is certainly not the first group to try and con Apple’s latest fondle-slab into letting them in. Wired went through a Herculean amount of effort in their attempt earlier in the month, only to get no farther than if they had just put a printed out picture of the victim in front of the camera. Details on how [Bkav] managed to succeed are fairly light, essentially boiling down to their claim that they are simply more knowledgeable about the finer points of face recognition than their competitors. Until more details are released, skepticism is probably warranted.
Still, even if their method is shown to be real and effective in the wild, it does have the rather large downside of requiring a 3D scan of the victim’s face. We’re not sure how an attacker is going to get a clean scan of someone without their consent or knowledge, but with the amount of information being collected and stored about the average consumer anymore, it’s perhaps not outside the realm of possibility in the coming years.
Since the dystopian future of face-stealing technology seems to be upon us, you might as well bone up on the subject so you don’t get left behind.
Thanks to [Bubsey Ubsey] for the tip.
Continue reading “Face ID Defeated With 3D Printed Mask (Maybe)”
Although it might be more accurate to say that this chair dances because no one is watching, the result is still a clever project that [Igor], a maker-in-residence at the National Museum of Decorative Arts and Design in Norway, created recently. Blurring the lines between art, hack, and the ghosts from Super Mario, this chair uses an impressive array of features to “dance”, but only if no one is looking at it.
In order to get the chair to appear to dance, [Igor] added servo motors in all four legs to allow them to bend. A small non-moving dowel was placed on the inside of the leg to keep the chair from falling over during all of the action. It’s small enough that it’s not immediately noticeable from a distance, which helps maintain the illusion of a dancing chair.
From there, a Raspberry Pi 3 serves as the control center for the chair. It’s programmed in Python and runs OpenCV for face detection and uses pigpio for controlling the leg servos. There’s also a web interface for watching the camera’s output and viewing its facial recognition abilities. The web interface also allows a user to debug the program. [Igor]’s chair can process up to 3 frames per second at 800×600 pixels.
Be sure to check out the video after the break to see the chair in action. It’s an interesting piece of art, and if those dowels can support the weight of a person it would be a great addition to any home as well. If it’s not enough chair for you, though, there are some other more dangerous options out there.
Continue reading “Chair Dances Like No One Is Watching”
[jwcrawley] is busy planning for the Makevention coming up in Bloomington, Indiana in late August. One problem when working any con is manning the door; it’s a good idea to know how many people are there, and you can’t double count people. Previously, the volunteers used dead trees to estimate how many people have turned up. This year they might go with a more technological solution: face recognition and tracking.
The project is called uWho, and it uses the faceRecognizer class in OpenCV. The purpose of the entire project is to identify who someone is from previous frames. If your face is unknown to the program, your likeness – rather, a few points of data – are added to the database of faces. It’s simple, and according to [jwcrawley], it works.
While this is technically the best way to count how many unique people show up to Makevention, there will be some discussions to see if this solution is appropriate. The program only saves unique data from a face locally, and does nothing online. It’s less evil than whatever Facebook does, but there are obvious privacy implications here.
Link to the Makevention.
Kegerator ownership is awesome, but it has its downsides. It’s hard to keep track of who drank what without cans or bottles to count. [Phil] was looking for a good solution to this problem when it came to sharing beer with his roommates and friends and has just completed the first iteration of his smart kegerator.
He has devised a system based on a Raspberry Pi. His software recognizes the face of the person pulling a beer and adds a charge to their tab based on the price of the keg and the volume of the pour. The system also keeps track of current and historic temperature and humidity values inside the kegerator, and everything is displayed on a Mimo 720S touch screen.
[Phil] has a flow meter on each keg to detect and monitor pouring. This triggers the Pi camera module to run the facial recognition. The walk-through found after the jump might be a bit confusing; at the time it was recorded, the unit was only capable of facial detection. [Phil] wrote the UI in QT and C++ and used Python scripts for the flow interrupts. His plans for future iterations include weight sensors underneath the kegs, liquid probe thermometers for more accurate beer temperature readings, a NoIR Pi camera module for low light conditions, and a really snazzy UI that you’ll see on his build page.
If you don’t have a Pi, here’s an Arduino-fied kegerator that reports temperature and controls beer cooling.
Continue reading “Smart Kegerator Bills Based on Beer Consumption”
Most of us have been faced with the anguish of being shot in the head repeatedly by 12-year-olds. There are also the times when we’re overjoyed by defeating the Mother Brain and making it out of the caverns of Zebes. If we wanted to scientifically quantify how happy, sad, or angry we are while playing video games, we wouldn’t know what to do. [Dale] came up with a very interesting way to gauge someone’s state of mind while either playing Xbox, or watching TV.
To get a measure of how happy or sad he is, [Dale] put a webcam underneath his TV and pointed it towards his couch. Every 15 seconds or so, the webcam snaps a picture and sends if off to the face.com API. After face.com sends a blob of JSON containing information about all the faces detected in the photo, a short Python script plots it on a graph.
[Dale] admits he’s not entirely scientific with this project; the low resolution of the webcam, coupled with images being captured every 15 seconds means he runs into the limitations of his hardware very quickly. Also, there’s the confound of [Dale] paying attention to something else in the room – like his kids – rather than the TV. Still, it’s an interesting use of hardware and software that would be loved by a market researcher or QA designer.
[Kyle McDonald] is trying out a new look, at least in the digital world, with the help of some openFrameworks video plugins. He’s working with [Arturo Castro] to make real-time facial substitution as realistic as possible. You can see that [Arturo’s] own video has a different take on shading and color of the facial alterations that makes them a bit less realistic than what [Kyle] was able to accomplish (see that clip after the break).
The setup depends on some facial tracking software developed by [Jason Saragih]. That package is wrapped in ofxFaceTracker (already linked at the top of this article) which makes it play nicely with openFrameworks. From there, it’s just a matter of image processing. If you think you’re up to the challenge, grab your own copies of the source code and get to work. We’re shocked by how real this looks, even when [Kyle] grabs his cheeks and stretches them out. If someone can fix some of the artifacts around the edges of the sampled faces this would be ready to use when video-conferencing.
It kind of makes us think of technology seen in The Running Man.
Continue reading “Get digital plastic surgery thanks to openFrameworks and some addons”
For those that are unaware, Androids are often judged by where they fall on the uncanny valley curve, a graph that maps human revulsion to robots that closely resemble humans but are just a bit off (similar to how a corpse resembles a living person). This offering jumps right over that dip of the curve and takes its rightful place as a human stand-in. Well, except that you’re probably going to notice the limbless torso… but pay no attention to the man behind the curtain!
This is the result of research by Geminoid Lab at Aalborg University. It is the twin of its creator and in an effort to be as human as possible, movements are mimicked using facial recognition from a human operator. We’d bet that with some clever learning routines you can map out and index common mannerisms from the original person for later use with this body-snatcher-esque copy. Take a look at the clips after the break; we don’t think you’ll be creeped out at all.
Continue reading “Android skips uncanny valley – fills in at the office for you”