Hackaday Links: October 13, 2019

Trouble in the Golden State this week, as parts of California were subjected to planned blackouts. Intended to prevent a repeat of last year’s deadly wildfires, which were tied in part to defective electrical distribution equipment, the blackouts could plunge millions in the counties surrounding Sacramento into the dark for days. Schools have canceled classes, the few stores that are open are taking cash only, and hospitals are running on generators. It seems a drastic move for PG&E, the utility that promptly went into bankruptcy after being blamed for last year’s fires, but it has the support of the governor, so the plan is likely to continue as long as the winds do. One group is not likely to complain, though;  California amateur radio operators must be enjoying a greatly decreased noise floor in the blackout areas, thanks to the loss of millions of switch-mode power supplies and their RF noise.

Good news, bad news for Fusion 360 users. Autodesk, the company behind the popular and remarkably capable CAD/CAM/CAE package, has announced changes to its licensing scheme, which went into effect this week. Users no longer have to pay for the “Ultimate” license tier to get goodies like 5-axis machining and generative design tools, as all capabilities are now included in the single paid version of Fusion 360. That’s good because plenty of users were unwilling to bump their $310 annual “Standard” license fee up to $1535 to get those features, but it’s bad because now the annual rate goes to $495. In a nice nod to the current userbase, those currently on the Standard license, as well as early adopters, will get to keep the $310 annual rate as long as they renew, and The $495 pricing tier went into effect in November of 2018, while anyone still on the $310 annual price was grandfathered in (and will remain to be). At that time there was still a $1535 tier called Ultimate, whose price will now be going away but the features remain in the $495 tier which is now the only pricing option for Fusion 360. Ultimate users will see a $1040 price drop. As for the current base of freeloaders like yours truly, fear not: Fusion 360 is still free for personal, non-commercial use. No generative design or tech support for us, though. (Editor’s Note: This paragraph was updated on 10/14/2019 to clarify the tier changes after Autodesk reached out to Hackaday via email.)

You might have had a bad day at the bench, but was it as bad as Román’s? He tipped us off to his nightmare of running into defective Wemos D1 boards – a lot of them. The 50 boards were to satisfy an order of data loggers for a customer, but all the boards seemed caught in an endless reboot loop when plugged into a USB port for programming. He changed PCs, changed cables, but nothing worked to stop the cycle except for one thing: touching the metal case of the module. His write up goes through all the dead-ends he went down to fix the problem, which ended up being a capacitor between the antenna and ground. Was it supposed to be there? Who knows, because once that cap was removed, the boards worked fine. Hats off to Román for troubleshooting this and sharing the results with us.

Ever since giving up their “Don’t be evil” schtick, Google seems to have really embraced the alternative. Now they’re in trouble for targeting the homeless in their quest for facial recognition data. The “volunteer research studies” consisted of playing what Google contractors were trained to describe as a “mini-game” on a modified smartphone, which captured video of the player’s face. Participants were compensated with $5 Starbucks gift cards but were not told that video was being captured, and if asked, contractors were allegedly trained to lie about that. Contractors were also allegedly trained to seek out people with dark skin, ostensibly to improve facial recognition algorithms that notoriously have a hard time with darker complexions. To be fair, the homeless were not exclusively targeted; college students were also given gift cards in exchange for their facial data.

For most of us, 3D-printing is a hobby, or at least in service of other hobbies. Few of us make a living at it, but professionals who do are often a great source of tips and tricks. One such pro is industrial designer Eric Strebel, who recently posted a video of his 3D-printing pro-tips. A lot of it is concerned with post-processing prints, like using a cake decorator’s spatula to pry prints off the bed, or the use of card scrapers and dental chisels to clean up prints. But the money tip from this video is the rolling cart he made for his Ultimaker. With the printer on top and storage below, it’s a great way to free up some bench space.

And finally, have you ever wondered how we hackers will rebuild society once the apocalypse hits and mutant zombie biker gangs roam the Earth? If so, then you need to check out Collapse OS, the operating system for an uncertain future. Designed to be as self-contained as possible, Collapse OS is intended to run on “field expedient” computers, cobbled together from whatever e-waste can be scrounged, as long as it includes a Z80 microprocessor. The OS has been tested on an RC2014 and a Sega Master System so far, but keep an eye out for TRS-80s, Kaypros, and the odd TI-84 graphing calculator as you pick through the remains of civilization.

The Tens Of Millions Of Faces Training Facial Recognition; You’ll Soon Be Able To Search For Yourself

In a stiflingly hot lecture tent at CCCamp on Friday, Adam Harvey took to the stage to discuss the huge data sets being used by groups around the world to train facial recognition software. These faces come from a variety of sources and soon Adam and his research collaborator Jules LaPlace will release a tool that makes these dataset searchable allowing you to figure out if your face is among the horde.

Facial recognition is the new hotness, recently bubbling up to the consciousness of the general public. In fact, when boarding a flight from Detroit to Amsterdam earlier this week I was required to board the plane not by showing a passport or boarding pass, but by pausing in front of a facial recognition camera which subsequently printed out a piece of paper with my name and seat number on it (although it appears I could have opted out, that was not disclosed by Delta Airlines staff the time). Anecdotally this gives passengers the feeling that facial recognition is robust and mature, but Adam mentions that this not the case and that removed from highly controlled environments the accuracy of recognition is closer to an abysmal 2%.

Images are only effective in these datasets when the interocular distance (the distance between the pupils of your eyes) is a minimum of 40 pixels. But over the years this minimum resolution has been moving higher and higher, with the current standard trending toward 300 pixels. The increase is not surprising as it follows a similar curve to the resolution available from digital cameras. The number of faces available in data sets has also increased along a similar curve over the years.

Adam’s talk recounted the availability of face and person recognition datasets and it was a wild ride. Of note are data sets by the names of Brainwash Cafe, Duke MTMC (multi-tracking-multi-camera),  Microsoft Celeb, Oxford Town Centre, and the Unconstrained College Students data set. Faces in these databases were harvested without consent and that has led to four of them being removed, but of course, they’re still available as what is once on the Internet may never die.

The Microsoft Celeb set is particularly egregious as it used the Bing search engine to harvest faces (oh my!) and has associated names with them. Lest you think you’re not a celeb and therefore safe, in this case celeb means anyone who has an internet presence. That’s about 10 million faces. Adam used two examples of past CCCamp talk videos that were used as a source for adding the speakers’ faces to the dataset. It’s possible that this is in violation of GDPR so we can expect to see legal action in the not too distant future.

Your face might be in a dataset, so what? In their research, Adam and Jules tracked geographic locations and other data to establish who has downloaded and is likely using these sets to train facial recognition AI. It’s no surprise that the National University of Defense Technology in China is among the downloaders. In the case of US intelligence organizations, it’s easier much easier to know they’re using some of the sets because they funded some of the research through organizations like the IARPA. These sets are being used to train up military-grade face recognition.

What are we to do about this? Unfortunately what’s done is done, but we do have options moving forward. Be careful of how you license images you upload — substantial data was harvested through loopholes in licenses on platforms like Flickr, or by agreeing to use through EULAs on platforms like Facebook. Adam’s advice is to stop populating the internet with faces, which is why I’ve covered his with the Jolly Wrencher above. Alternatively, you can limit image resolution so interocular distance is below the forty-pixel threshold. He also advocates for changes to Creative Commons that let you choose to grant or withhold use of your images in train sets like these.

Adam’s talk, MegaPixels: Face Recognition Training Datasets, will be available to view online by the time this article is published.

AI On Raspberry Pi With The Intel Neural Compute Stick

I’ve always been fascinated by AI and machine learning. Google TensorFlow offers tutorials and has been on my ‘to-learn’ list since it was first released, although I always seem to neglect it in favor of the shiniest new embedded platform.

Last July, I took note when Intel released the Neural Compute Stick. It looked like an oversized USB stick, and acted as an accelerator for local AI applications, especially machine vision. I thought it was a pretty neat idea: it allowed me to test out AI applications on embedded systems at a power cost of about 1W. It requires pre-trained models, but there are enough of them available now to do some interesting things.

You can add a few of them in a hub for parallel tasks. Image credit Intel Corporation.

I wasn’t convinced I would get great performance out of it, and forgot about it until last November when they released an improved version. Unambiguously named the ‘Neural Compute Stick 2’ (NCS2), it was reasonably priced and promised a 6-8x performance increase over the last model, so I decided to give it a try to see how well it worked.

 

I took a few days off work around Christmas to set up Intel’s OpenVino Toolkit on my laptop. The installation script provided by Intel wasn’t particularly user-friendly, but it worked well enough and included several example applications I could use to test performance. I found that face detection was possible with my webcam in near real-time (something like 19 FPS), and pose detection at about 3 FPS. So in accordance with the holiday spirit, it knows when I am sleeping, and knows when I’m awake.

That was promising, but the NCS2 was marketed as allowing AI processing on edge computing devices. I set about installing it on the Raspberry Pi 3 Model B+ and compiling the application samples to see if it worked better than previous methods. This turned out to be more difficult than I expected, and the main goal of this article is to share the process I followed and save some of you a little frustration.

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Hackaday Podcast Ep1 – Seriously, We Know What We’re Doing

First podcast of the new year! Editors Elliot Williams and Mike Szczys look back on the most interesting hacks and can’t-miss articles from the past week (or so). Highlights include abusing IPv6 addresses, underclocking WiFi, taking Wii out of the livingroom, scratch built microphones, computer prophecy coming true, and the end of an automotive era. Full show notes below.

This week, Hackaday Contributor Bob Baddeley came on the show to discuss developments in facial recognition technology and its use in the wild.

Direct Download (45.1 MB MP3)

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Your Face Is Going Places You May Not Like

Many Chinese cities, among them Ningbo, are investing heavily in AI and facial recognition technology. Uses range from border control — at Shanghai’s international airport and the border crossing with Macau — to the trivial: shaming jaywalkers.

In Ningbo, cameras oversee the intersections, and use facial-recognition to shame offenders by putting their faces up on large displays for all to see, and presumably mutter “tsk-tsk”. So it shocked Dong Mingzhu, the chairwoman of China’s largest air conditioner firm, to see her own face on the wall of shame when she’d done nothing wrong. The AIs had picked up her face off of an ad on a passing bus.

False positives in detecting jaywalkers are mostly harmless and maybe even amusing, for now. But the city of Shenzhen has a deal in the works with cellphone service providers to identify the offenders personally and send them a text message, and eventually a fine, directly to their cell phone. One can imagine this getting Orwellian pretty fast.

Facial recognition has been explored for decades, and it is now reaching a tipping point where the impacts of the technology are starting to have real consequences for people, and not just in the ways dystopian sci-fi has portrayed. Whether it’s racist, inaccurate, or easily spoofed, getting computers to pick out faces correctly has been fraught with problems from the beginning. With more and more companies and governments using it, and having increasing impact on the public, the stakes are getting higher.

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3D Printed Head Can Unlock Your Phone

[Thomas Brewster] writes for Forbes, but we think he’d be at home with us. He had a 3D printed head made in his own image and then decided to see what phones with facial recognition he could unlock. Turns out the answer is: most of them — at least, those running Android.

The models tested included an iPhone X, an LG, two Samsung phones, and a OnePlus. Ironically, several of the phones warn you when you enroll a face that the method may be less secure than other locking schemes. Conversely, one phone had a faster feature that is known to make the phone less secure.

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Raspberry Pi Offers Soulless Work Oversight

If you’re like us, you spend more time than you care to admit staring at a computer screen. Whether it’s trying to find the right words for a blog post or troubleshooting some code, the end result is the same: an otherwise normally functioning human being is reduced to a slack-jawed zombie. Wouldn’t it be nice to be able to quantify just how much of your life is being wasted basking in the flickering glow of your monitor? Surely that wouldn’t be a crushingly depressing piece of information to have at the end of the week.

With the magic of modern technology, you need wonder no longer. Prolific hacker [dekuNukem] has created the aptly named “facepunch”, which allows you to “punch in” with nothing more than your face. Just sit down in front of your Raspberry Pi’s camera, and the numbers start ticking away. It’s like the little clock in the front of a taxi: except at the end you don’t have to pay anyone, you just have to come to terms with what your life has become. So that’s cool.

It doesn’t take much hardware to play along at home. All you need is a Raspberry Pi and the official camera accessory. Though for the full effect you should add one of the displays supported by the Luma.OLED driver so you can see the minutes and hours ticking away in real-time.

To get the facial recognition going, all you need to do is take a well-lit picture of your face and save it as a 400×400 JPEG. The Python 3 script will take care of the rest: checking the frames from the camera every few seconds to see if your beautiful mug is in the frame, and incrementing the counters accordingly.

Even if you’re not in the market for an Orwellian electronic supervisor, this project is a great example to get you started in the world of facial recognition. With a little luck, you’ll be weaponizing it in no time.