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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R3-14, The Personal Assistant Two Years In The Making

One of the great things about hacking together projects these days is how many powerful subsystems are readily available to reuse. [Sanjeet] took full advantage of a whole slate of reusable pieces when he built R3-14 — a personal assistant robot that you can see in action in the video below.

Many people started out in electronics building something simple like a crystal radio or an LED cube. But how far could you get if your projects had to begin at the most basic level, by drawing out copper wire, fabricating coils, capacitors, semiconductor devices, and batteries? Even if you know how to do all those things, it would take a lot of time, so there is no shame in using off-the-shelf components. By the same token, [Sanjeet] uses Google Assistant, 433 MHz RF transmitters, and a Raspberry Pi as components in this build. Along the way, he also contributed some reusable pieces himself, including an LED library for the PI and a library to allow Siri to control a Raspberry Pi.

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Hackaday Prize Entry: Selfie Bot Let’s You Vlog Hands Free

[Sergey Mironov] sent in his SelfieBot project. His company, Endurance Robots, sells a commercial version of the bot, which leads us to believe that in a strange and maybe brilliant move he decided to just sell the prototype stage of the product development as a kit. Since he also gave away the firmware, STLs, BOM, and made a guide so anyone can build it, we’re not complaining.

The bot is simple enough. Nicely housed hobby servos in a 3D printed case take care of the pan and tilt of the camera. The base of the bot encloses the electronics, which are an Arduino nano, a Bluetooth module, and the support electronics for power and motor driving.

To perform the face tracking, the build assumes you have a second phone. This is silly, but isn’t so unreasonable. Most people who’ve had a smart phone for a few years have a spare one living in a drawer as back-up. One phone runs the face tracking software and points the bot, via Bluetooth, towards the user. The other phone records the video.

The bot is pretty jumpy in the example video, but this can be taken care of with better motors. For a proof-of-concept, it works. A video of it in action after the break.

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Hexapod Tank from Ghost in the Shell Brought to Life

Every now and then someone gets seriously inspired, and that urge just doesn’t go away until something gets created. For [Paulius Liekis], it led to creating a roughly 1:20 scale version of the T08A2 Hexapod “Spider” Tank from the movie Ghost in the Shell. As the he puts it, “[T]his was something that I wanted to build for a long time and I just had to get it out of my system.” It uses two Raspberry Pi computers, 28 servo motors, and required over 250 hours of 3D printing for all the meticulously modeled pieces – and even more than that for polishing, filing, painting, and other finishing work on the pieces after they were printed. The paint job is spectacular, with great-looking wear and tear. It’s even better seeing it in motion — see the video embedded below.

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Googly Eyes Follow You Around the Room

If you’re looking to build the next creepy Halloween decoration or simply thinking about trying out OpenCV for the first time, this next project will have you covered. [Glen] made a pair of giant googly eyes that follow you around the room using some servos and some very powerful software.

The project was documented in three parts. In Part 1, [Glen] models and builds the eyes themselves, including installing the servo motors that will eventually move them around. The second part involves an Arduino and power supply that will control the servos, and the third part goes over using OpenCV to track faces.

This part of the project is arguably the most interesting if you’re new to OpenCV; [Glen] uses this software package to recognize different faces. From there, the computer picks out the most prominent face and sends commands to the Arduino to move the eyes to the appropriate position. The project goes into great detail, from Arduino code to installing Ubuntu to running OpenCV for the first time!

We’ve featured some of [Glen]’s projects before, like his FPGA-driven LED wall, and it’s good to see he’s still making great things!

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The Face-Tracking Confectionery Cannon!

A team of mechanical and electrical engineering students at Olin College came up with a very fun semester project — a pneumatic powered marshmallow cannon that can track faces, and aim for the mouth!

The device — dubbed the Confectionery Canon — is an impressive mechanical build which required many of Olin College’s manufacturing resources such as the laser cutter, the mill, and the lathe. The majority of the device was made out of acrylic, which was chosen for easy laser cutting, and affordability. Specific aluminum pieces provide strength and were made using mostly scrap found in the shop.

Four servos, a webcam, a solenoid and an Arduino Uno make up the electrical system, which uses Python and OpenCV to track faces (GitHub). A PVC tank is used as the pneumatic reservoir, charged with a safety release valve at 30PSI. To fire the cannon, a sprinkler valve is controlled by a beefy solenoid. It currently only has a magazine capacity of 4 large marshmallows, but the team is planning on upgrading soon.

They have put together a great website with tons of information on the project, and following the break is a fun promo video they made for the project — they even got the VP of  the college to try it!

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Two computer vision builds from Cornell

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[Bruce Land], professor at Cornell, is a frequent submitter to our tip line. Usually he sends in a few links every semester from undergraduate electronics courses. Now the fall semester is finally over and it’s time to move on to the more ambitious master’s projects.

First up is a head-mounted eye tracker, [Anil Ram Viswanathan] and [Zelan Xiao] put together a lightweight and low-cost eye tracking project that will record where the user is looking.

The eye tracker hardware is made of two cameras mounted on a helmet. The first camera faces forward, looking at the same thing the user is. The second camera is directed towards the user’s eye. A series of algorithms detect the iris of the user’s eye and overlays the expected gaze position on the output of the first camera. Here’s the design report. PDF, natch.

Next up is a face tracking project implemented on an FPGA. This project started out as a software implementation of a face tracking algorithm in MATLAB. [Thu-Thao Nguyen] translated this MATLAB code to Verilog and eventually got her hardware running on an FPGA dev board. Another design report.

Having a face detection and tracking system running on an FPGA is extremely interesting; the FPGA makes face tracking a very low power and hopefully lower-cost solution, allowing it to be used in portable and consumer devices.

You can check out the videos for these projects after the break.

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