New Facial Recognition Software Can Reconstruct a Face From Minimal Photo Data
Hoping to aid law enforcement, Savvides’ team took the released photo from the FBI website and ran it through an early version of the enhancement software. The software is a machine learning system “trained” with a database of 30,000 faces presented in multiple resolutions. The algorithms constructed through training can draw from the system’s experience and reconstruct an approximation of a face based on patterns within facial images, with as little as six pixels between the eyes of a suspect. As a result of the training, the software can essentially reconstruct a face based on the relationship between pixels and human-assisted identification of facial landmarks, producing what Savvides called a “hallucination” of the individual’s face from negligible amounts of image data.
(via “Hallucinating” a face, new software could have ID’d Boston bomber | Ars Technica)
DARPA Funded Research Aims to Automate Video Surveillance, Teach Computers to Identify Suspicious Behavior
This approach relies heavily on advances by machine vision researchers, who have made remarkable strides in last few decades in recognizing stationary and moving objects and their properties.
It’s the same vein of work that led to Google’s self-driving cars, face recognition software used on Facebook and Picasa, and consumer electronics like Microsoft’s Kinect.
When it works well, machine vision can detect objects and people — call them nouns — that are on the other side of the camera’s lens. But to figure out what these nouns are doing, or are allowed to do, you need the computer science equivalent of verbs.
…that’s where Oltramari and Lebiere have built on the work of other Carnegie Mellon researchers to create what they call a “cognitive engine” that can understand the rules by which nouns and verbs are allowed to interact. Their cognitive engine incorporates research, called activity forecasting, …which tries to understand what humans will do by calculating which physical trajectories are most likely.
They say their software “models the effect of the physical environment on the choice of human actions.”
Both projects are components of Carnegie Mellon’s Mind’s Eye architecture, a DARPA-created project that aims to develop smart cameras for machine-based visual intelligence. Predicts Oltramari: “This work should support human operators and automatize video-surveillance, both in military and civil applications.”
(via U.S. looks to replace human surveillance with computers | Security & Privacy - CNET News)
New Software Mashes Up Crowdsourced Video to Personalize Event Recordings
Cesar starts with raw video footage recorded at a specific event by different people, at different moments and from different perspectives. Next, software called a “narrative engine” uses what it knows of the relationships between people to create dynamic stories. These are tailored to an individual’s preferences, interests and social connections by automatically stitching together parts of clips into a seamless video stream.
“The stories are highly personal depending on the recipient of the story,” says Cesar.
The system works by synchronising all the video clips with a master audio track that is recorded at the event. The audio of each clip gives it a digital fingerprint allowing similar footage in different clips to be matched up. The software analyses the video content - applying facial recognition techniques, for example - and contextual information added by the film-maker. It then puts together clips or partial clips, producing a bespoke video edit for every user.
The system was tested at a school concert that was filmed with 12 cameras - some fixed, some belonging to parents in the audience - generating more than 300 raw video clips. These clips were pooled and annotated with personal details, including who was in the clip, or which musical instrument was shown. Most parents agreed that the tailored films made the viewing experience more personal.
The team presented the work at the 2012 Symposium on Document Engineering in Paris, earlier this month. “We’re living in a world of abundant content,” says Mor Naaman of Mahaya, a start-up that is developing software to find and organise social media shared from real-world events. “The real technical challenge is to do this at scale.”
(via Video mash-ups give you personalised memories - tech - 21 September 2012 - New Scientist)
CV Dazzle: A Fashion/Design Response to The Surveillance State
As facial recognition technologies become ubiquitous, the creative community is shapes a response that is a design as well as political statement.
From the mission statement at cvdazzle.com
CV Dazzle™ is camouflage from computer vision (CV). It is a form of expressive interference that combines makeup and hair styling (or other modifications) with face-detection thwarting designs. The name is derived from a type of camouflage used during WWI, called Dazzle, which was used to break apart the gestalt-image of warships, making it hard to discern their directionality, size, and orientation. Likewise, the goal of CV Dazzle is to break apart the gestalt of a face, or object, and make it undetectable to computer vision algorithms, in particular face detection.
(ht cyberpunkx, ht inspired—motivated)
FBI Building Massive Facial Recognition Database
As part of an update to the national fingerprint database, the FBI has begun rolling out facial recognition to identify criminals. It will form part of the bureau’s long-awaited, $1 billion Next Generation Identification (NGI) programme, which will also add biometrics such as iris scans, DNA analysis and voice identification to the toolkit.
A handful of states began uploading their photos as part of a pilot programme this February and it is expected to be rolled out nationwide by 2014.
In addition to scanning mugshots for a match, FBI officials have indicated that they are keen to track a suspect by picking out their face in a crowd.
Another application would be the reverse: images of a person of interest from security cameras or public photos uploaded onto the internet could be compared against a national repository of images held by the FBI. An algorithm would perform an automatic search and return a list of potential hits for an officer to sort through and use as possible leads for an investigation.
Ideally, such technological advancements will allow law enforcement to identify criminals more accurately and lead to quicker arrests. But privacy advocates are worried by the broad scope of the FBI’s plans. They are concerned that people with no criminal record who are caught on camera alongside a person of interest could end up in a federal database, or be subject to unwarranted surveillance.
(via FBI launches $1 billion face recognition project - tech - 07 September 2012 - New Scientist)
Set-Top Box Uses Facial Recognition to Target Ads to Whomever is Watching
Intel has developed a new set-top box that will monitor who is watching TV at that moment, enabling advertisers to target the most appropriate ads to them. The box does this by using facial recognition technology.
(ht paulocesilveira)
Real Time Avatar Controller Uses Web Cam to Reproduce Facial Expressions, Body Language
To detect and track faces, this system uses time-series signal processing. It tracks characteristic points, including the eyes, nose, and mouth, at high speed with high precision. The white dots on the screen show the points used to track the face, and the red line shows the orientation of the face. So you can see that the system is detecting the face appropriately, in line with the way it’s facing and the movement of the mouth.
“We’re using an algorithm that gets updated in line with the motion of the face. So it can track the face very fast, with very high precision. That’s the basic technology for this avatar system.”
This system also analyzes the shape of the person’s expression. So it can reproduce how the eyebrows and mouth are moving, and whether the person is laughing, angry, or surprised.
As well as avatars, this system could also be used for games that detect and react to changes in people’s faces.
(via diginfo ht futurescope)
Computers Better at Detecting Phoniness Than Humans
A study has found that most people unwittingly smile when frustrated. What’s more, it turns out that computers programmed with the latest information from research do a better job of differentiating smiles of delight and frustration than human observers do.
Source: MIT
Read more: http://www.laboratoryequipment.com/news-Computer-Spots-Fake-Real-Smiles-052912.aspx
(via skeptv)
Computers Using Facial Recognition Identify Liars 30% Better Than Trained Investigators:
Researchers at the University at Buffalo, The State University of New York (U.B.), claim their video-analysis software can analyze eye movement successfully to identify whether or not a subject is fibbing 82.5 percent of the time. The researchers, who first presented their (still unpublished) results at the 2011 IEEE International Conference on Automatic Face and Gesture Recognition a year ago, believe they have laid the foundation for a more extensive study that will include a larger sample and take into account body language in addition to eye movement to determine whether new technologies can help interrogators in their search for the truth.
(via In-Your-Face: Can Computers Catch You Telling a Lie?: Scientific American)
