"Computers have been getting better and better at seeing movement on video. How is it that they read lips, follow a dancing girl or copy an actor making faces?"
– from Andrew Blake. Introduction to Active Contours and Visual Dynamics. Visual Dynamics Group, Department of Engineering Science, University of Oxford
With images aggregated from social media platforms, dating sites, or even CCTV footage of a trip to the local coffee shop, companies could be using your face to train a sophisticated facial recognition software. As reported by the New York Times, among the sometimes massive data sets that researchers use to teach artificially intelligent software to recognize faces is a database collected by Stanford researchers called Brainwash. More than 10,000 images of customers at a cafe in San Francisco were collected in 2014 without their knowledge. OKCupid and photo-sharing platforms like Flickr are among for researchers looking to load their databases up with images that help train facial recognition software. That same database was then made available to other academics, including some in China at the National University of Defense Technology.
Better known as a supplier of facial recognition software used by the Chinese government, an AI-startup that is backed by Alibaba has developed software that can identify dogs by their noses. No, it isn't April 1st; the facial recognition software developed by Megvii really can identify one dog from another by using nasal biometrics. KrAsia news reports that the company has developed the software on the basis that dogs have unique nose prints. Dr. David Dorman, a professor of toxicology, has previously said that: "Like human fingerprints, each dog has a unique nose print. Some kennel clubs have used dog nose prints for identification."
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classification system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. We find that these datasets are overwhelmingly composed of lighter-skinned subjects (79.6% for IJB-A and 86.2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type. We evaluate 3 commercial gender classification systems using our dataset and show that darker-skinned females are the most misclassified group (with error rates of up to 34.7%).
Roundup Hello, here's a few announcements from the world of machine learning beyond what we've already covered this week. AlphaStar is coming out to play: AlphaStar, the StarCraft II-playing bot built by DeepMind researchers, will be facing human players in a series of 1v1 games online. StarCraft II players can enter the open competition league set up by Blizzard Entertainment, the creators of the popular battle strategy game, and opt-in to play against AlphaStar. But nobody will know if they're facing the bot, however, because it'll be entering the matches anonymously. Characters in the StarCraft II are from three species: Terran, Zerg or Protoss.
The sophisticated technology that powers face recognition in many modern smartphones someday could receive a high-tech upgrade that sounds--and looks--surprisingly low-tech. This window to the future is none other than a piece of glass. University of Wisconsin-Madison engineers have devised a method to create pieces of "smart" glass that can recognize images without requiring any sensors or circuits or power sources. "We're using optics to condense the normal setup of cameras, sensors and deep neural networks into a single piece of thin glass," says UW-Madison electrical and computer engineering professor Zongfu Yu. Yu and colleagues published details of their proof-of-concept research today in the journal Photonics Research.
Dozens of databases of people's faces are being compiled without their knowledge by companies and researchers, with many of the images then being shared around the world, in what has become a vast ecosystem fueling the spread of facial recognition technology. The databases are pulled together with images from social networks, photo websites, dating services like OkCupid and cameras placed in restaurants and on college quads. While there is no precise count of the data sets, privacy activists have pinpointed repositories that were built by Microsoft, Stanford University and others, with one holding over 10 million images while another had more than two million. The face compilations are being driven by the race to create leading-edge facial recognition systems. This technology learns how to identify people by analyzing as many digital pictures as possible using "neural networks," which are complex mathematical systems that require vast amounts of data to build pattern recognition.
Megvii, a Chinese AI startup that supplies facial recognition software for the Chinese government's surveillance program, is expanding its technology beyond humans to recognize different faces of pets. As reported by Abacus News, Megvii's new program is trained to recognize dogs by their nose prints -- much like how humans have unique fingerprints. Using the Megvii app, the company says it can register your dog simply by scanning the snout through your phone's camera. Just like how a phone registers your fingerprint for biometric unlocks, the app asks you to take photos of your dog's nose from multiple angles. Megvii says it has an accuracy rate of 95 percent and has reunited 15,000 pets with their owners through the app.
An artificial intelligence (AI)-trained facial recognition system (FRS) has been installed at the Puratchi Thalaivar Dr. MGR Central railway station for detecting known culprits passing through the gates and alerting authorities. "For the first time, we have introduced the CCTV camera device backed by artificial intelligence. In the existing system, we capture the picture and video of any suspect. But we have to manually analyse the footage to detect their movement. The new system will automatically alert us about known culprits," said a senior police officer of the Government Railway Police (GRP).
Facial recognition is a booming business! It has transformed the way we live in 2019, opening up exciting possibilities and posing new dangers. At present, we use facial recognition when unlocking a smartphone or laptop, but it will soon play a major role in everything right from booking a taxi to ordering food. Facial recognition is a form of biometric authentication that uses body measurements to verify your identity. It identifies people by measuring the unique shape and structure of the face.
Facial recognition could be used to replace swipe cards on public transport, the New South Wales government has suggested, but the opposition and digital rights groups say it would pose a risk to privacy. The transport minister, Andrew Constance, said on Tuesday he wanted commuters "in the not too distant future" to be able to board trains using only their faces, with no need for Opal cards, barriers or turnstiles. "I'm about to outline some concepts which may seem pretty crazy and far-fetched," he told the Sydney Institute on Tuesday. "But look at it this way – who would have thought in 1970 that you'd be able to use a handheld device to have a video conversation with someone on the other side of the world? "I want people to not think about their travel.