Face tracking with an Android device

This Android device can recognize faces and move to keep them in frame. It’s a proof of concept that uses commonly available parts and software packages.

The original motivation for the project was [Dan O's] inclination to give the OpenCV software a try. OpenCV is an Open Source Computer Vision package that takes on the brunt of the job when it comes to discerning meaning from images. To give the phone the power to move he designed and printed his own mounting brackets for the phone and a couple of hobby servos. An IOIO board connects to the Android device in order to control the motors. On the software side all [Dan] needed to do was write some code to interface the output of the OpenCV face tracking modules with the input of the IOIO. See the finished project demonstration after the jump.

This system can easily be implemented with other hardware, like this Arduino-based version we looked at earlier in the year.

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Smile, your face is on the Internet

[Kyle McDonald] is up to a bit of no-good with a little piece of software he wrote. He’s been installing it on public computers all over New York City. It uses the webcam found in pretty much every new computer out there to detect when a face is in frame, then takes a picture and uploads it to the Internet.

We’ve embedded a video after the break that describes the process. From [Kyle's] comments about the video it seems that he asked a security guard at the Apple store if it was okay to take pictures and he encouraged it. We guess it could be worse, if this were a key logger you’d be sorry for checking your email (or, god forbid, banking) on a public machine. Instead of being malicious, [Kyle] took a string of the images, adjusted them so that the faces were all aligned and the same size, and then rolled them into the latter half of his video.

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Cheap and reliable portable face recognition system

faceaccess_portable_facial_recognition

For their senior ECE 4760 project, engineering students [Brian Harding and Cat Jubinski] put together a pretty impressive portable face recognition system called FaceAccess. The system relies on the eigenface method to help distinguish one user from another, a process that the pair carried out using MatLab.

They say that the system only needs to be hooked up to a computer once, during the training period. It is during this period that faces are scanned and processed in MatLab to create the eigenface set, which is then uploaded to the scanner.

Once programmed, the scanner operates independently of the computer, powered by its own ATmega644 micro controller. Users enroll their face by pressing one button on the system, storing their identity as a combination of eigenfaces in the onboard flash chip. Once an individual has been enrolled, a second button can be pressed to gain access to whatever resources the face recognition system is protecting.

The students say that their system is accurate 88% of the time, with zero false positives – that’s pretty impressive considering the system’s portability and cost.

Stick around to see a quick demo video of their FaceAccess system in action.

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Camera software learns to pick you out of a crowd

tld_object_tracking

While the Kinect is great at tracking gross body movements and discerning what part of a person’s skeleton is moving in front of the camera, the device most definitely has its shortfalls. For instance, facial recognition is quite limited, and we’re guessing that it couldn’t easily track an individual’s eye throughout the room.

No, for tracking like that, you would need something far more robust. Under the guidance of [Krystian Mikolajczyk and Jiri Matas], PhD student [Zdenek Kalal] has been working on a piece of software called TLD, which has some pretty amazing capabilities. The software uses almost any computer-connected camera to simultaneously Track an object, Learn its appearance, and Detect the object whenever it appears in the video stream. The software is so effective as you can see in the video below, that it has been dubbed “Predator”.

Once he has chosen an object within the camera’s field of vision, the software monitors that object, learning more and more about how it looks under different conditions. The software’s learning abilities allow it to pick out individual facial features, follow moving objects in video, and can recognize an individual’s face amid a collection of others.

While the software can currently only track one object at a time, we imagine that with some additional development and computing horsepower, this technology will become even more amazing.

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