"... the research area that studies the operation and design of systems that recognize patterns in data." It includes statistical methods like discriminant analysis, feature extraction, error estimation, cluster analysis.
– Pattern Recognition Laboratory at Delft University of Technology
A year ago, when Apple rolled out the iPhone X, one of their most touted features was facial ID. You no longer needed to press a home button or use a passcode. You could unlock your phone with your face. It was the first time I'd really seen facial recognition software being practically used. You probably use something every day with facial recognition software even if you don't realize it--I'm looking at you Snapchat and Instagram face filters.
In this post we are going to develop a java face recognition application using deeplearning4j. The application is offering a GUI and flexibility to register new faces so feel free to try with your own images. Additionally you can check out the free open source code as part of the PactPub video course Java Machine Learning for Computer Vision together with many new improvements to previous posts applications in java. Face recognition has always been an important problem to solve due its sensitivity in regards to security and because it closely related to people identity. For many years face recognition applications were well known especially in criminology and searching for wanted persons with cameras and sometimes even using satellites.
As facial recognition technology use generates intense scrutiny, a new system unveiled at Washington's Dulles airport is being touted as a "user friendly" way to help ease congestion for air travelers. Officials at Dulles unveiled two new face recognition systems Thursday, one to meet legal requirements for biometric entry-exit records, and a second to help speed processing of travelers arriving on international flights by matching their real-time images with stored photos. The growing use of facial recognition has ignited debate over surveillance and privacy around the world, but officials told media this system was a way to help reducing annoying lines and wait times without compromising security. "The technology works," US Customs and Border Protection Commissioner Kevin McAleenan told reporters at an airport unveiling. And we believe it will change the face of international travel."
LAST December, Ed Bridges was mingling with the crowds of Christmas shoppers on the streets of Cardiff, UK, when the police snapped a picture of him. He has been trying to get them to delete it ever since. Bridges hasn't been convicted of a crime, nor is he suspected of committing one. He is simply one of a vast number of people who have been quietly added to face-recognition databases without their consent, and most often, without their knowledge.
Researchers at Facebook offered up a summary of a system they call "Rosetta," a machine learning approach that boosts traditional optical character recognition, or "OCR," to mine the hundreds of millions of photos uploaded to Facebook daily. Say you want to search for memes in images on Facebook: The site's challenge is to detect whether there are letters printed within an image, and then parse those letters to know what a phrase says. This technology has, of course, been in use for document processing for ages, but the challenge at Facebook was both to recognize text in any number of complex images, including text laid over the image, as in an internet meme or text such as a sign that was part of the original image, and then to make it work at the scale of the site's constant stream of images. Facebook researchers Fedor Borisyuk, Albert Gordo, and Viswanath Sivakumar shared the work on Rosetta at the Knowledge Discovery and Data Mining conference in London in late August, in a formal paper, and today, two of the authors, Gordo and Sivakumar, along with Facebook's Manohar Paluri, offered up a somewhat simpler blog post describing the work. Facebook split up the task of "extracting" text from an image into two separate matters, that of first detecting whether there is text at all in an image, and then to parsing what that word of phrase might be.
Facebook's moderators can't possibly look through every single image that gets posted on the enormous platform, so Facebook is building AI to help them out. In a blog post today, Facebook describes a system it's built called Rosetta that uses machine learning to identify text in images and videos and then transcribe it into something that's machine readable. In particular, Facebook is finding this tool helpful for transcribing the text on memes. Text transcription tools are nothing new, but Facebook faces different challenges because of the size of its platform and the variety of the images it sees. Rosetta is said to be live now, extracting text from 1 billion images and video frames per day across both Facebook and Instagram.
Credit institutions are poised to use a combination of artificial intelligence and facial recognition to instantly read the facial expressions of applicants to assess their likelihood of loan repayment. The South China Morning Post reports that Ping An Puhui, a Chinese micro lending unit of China's second-largest life insurer, has developed a digitalized loan process that can "analyse facial expressions of applicants to determine their willingness to repay the loans." The company contends that as a result of using new technologies, including facial recognition and big data, it has seen its customers "more than doubling to 5.5 million from 2 million a year ago," and its loan default rate drop, without the necessity of expanding its staffing. Facial recognition for identity verification and mobile payments just got a big boost with the latest iPhone launch. Thanks to Apple, consumers will become far more accustomed to the use of facial recognition for identity verification and digital payments.
Tech went to Washington this week, and their biggest problems followed them. Twitter CEO Jack Dorsey and Facebook COO Sheryl Sandberg faced Congress, and though Google CEO Larry Paige was invited, he declined to make the trip--a move that didn't ingratiate him with Congressional watchdog Mark Warner. One uninvited guest did make an appearance at the hearings, however: Alex Jones. He heckled Dorsey and a CNN reporter, and was captured by a photographer's lens for what is one of the most perfect (and surreal) photos of 2018. Though Jones' DC antics were mild compared with his past bad behavior, being that physically close to his trolling seems to have finally woken up Dorsey; Twitter permanently banned Jones the next day.
One year ago, Craig Federighi opened his eyes, stared into the brand-new iPhone X, and showed the world how he could unlock it with his face. Or, at least, he tried. It took the Apple executive a few attempts and one back-up phone to get the screen to unlock without a fingerprint or a passcode. But then, like magic, he was in. This was Apple's annual fall hardware show, where the company dangles its newest iPhones before the world and sets the tone for consumer products to come.
As the Trump administration continues to advance its hardline stance towards immigration, legal or otherwise, businesses are increasingly turning to automation and robotics to fill jobs previously held by humans. However, these thinking machines are not without drawbacks. AI development has long been beset by issues of intrinsic training bias, if not outright racist and xenophobic behavior. Take Microsoft's aborted social media bots Tay and Zo, for example, or Amazon's questionably-designed facial recognition system. However this relationship is not unidirectional -- AI can impact the expression of xenophobic ideas just as xenophobic practices can impact the rate of AI development.