"What exactly is computer vision then? Computer vision is a research field working to equip computers with the ability to process and understand visual data, as sighted humans can. Human brains process the gigabytes of data passing through our eyes every second and translate that data into sight - that is, into discrete objects and entities we can recognise or understand. Similarly, computer vision aims to give computers the ability to understand what they are seeing, and act intelligently on that knowledge."
– Computer vision: Cheat Sheet. ZDNet.com (December 6, 2011), by Natasha Lomas.
Amazon is offering up $25 gift cards in exchange for 3D scans of your body. The internet giant is currently conducting a study at its New York office as part of Amazon Body Labs that seeks to'learn about diversity among body shapes,' according to a listing, which was first spotted by Mashable. Participants who set up a 30-minute appointment will be asked to take a survey and agree to have 3D scans, photos and videos taken of them. The move comes as Amazon has faced privacy concerns around its collection of data from Echo devices, as well as the deployment of its controversial facial recognition software. Amazon is offering up $25 gift cards in exchange for 3D scans of your body.
Megvii Technology, a Chinese company, founded in 2011 and widely known for its Face system, is one of the world leaders in facial recognition and artificial intelligence technology. While they might be best known for Face, Megvii uses artificial intelligence and machine vision in a variety of amazing ways. Megvii was the concept conceived by friends and Tsinghua University graduates Yin Qui, Yang Mu, and Tang Wenbin. After tremendous success in China (especially since they were able to train algorithms from China's vast pool of data) with clients such as Ant Financial, Vivo (smartphones), Didi Chuxing (ride-sharing) and investments from Bank of China, the State-Owned Venture Capital Fund, China-Russian Investment Fund and other private investors including Ant Financial (Alibaba's payment affiliate), Megvii is ready to go global. They have projects slated in the coming year for Japan, Europe, the Middle East, Southeast Asia, and the United States and have secured a distributor in Thailand.
The information commissioner has expressed concern over the lack of a formal legal framework for the use of facial recognition cameras by the police. A barrister for the commissioner, Elizabeth Denham, told a court the current guidelines around automated facial recognition (AFR) technology were "ad hoc" and a clear code was needed. In a landmark case, Ed Bridges, an office worker from Cardiff, claims South Wales police violated his privacy and data protection rights by using AFR on him when he went to buy a sandwich during his lunch break and when he attended a peaceful anti-arms demonstration. The technology maps faces in a crowd and then compares them with a watchlist of images, which can include suspects, missing people or persons of interest to the police. The cameras have been used to scan faces in large crowds in public places such as streets, shopping centres, football crowds and music events such as the Notting Hill carnival.
A growing backlash against face recognition suggests the technology has a reached a crucial tipping point, as battles over its use are erupting on numerous fronts. Face-tracking cameras have been trialled in public by at least three UK police forces in the last four years. A court case against one force, South Wales Police, began earlier this week, backed by human rights group Liberty. Ed Bridges, an office worker from Cardiff whose image was captured during a test in 2017, says the technology is an unlawful violation of privacy, an accusation the police force denies. Avoiding the camera's gaze has got others in trouble.
Researchers from Samsung's AI Centre located in Moscow have created a new system that can transform still facial images into video sequences of the human face making speech expressions. According to the study, the system creates realistic virtual talking heads through applying the facial landmarks of a target face onto a source face -- for example, a still photo -- to allow the target face to control how the source face moves. "Such ability has practical applications for telepresence, including videoconferencing and multi-player games, as well as [the] special effects industry," Samsung said. While the existence of "deepfake" technology isn't something new, Samsung's new system does not use 3D modelling and only requires one photograph to create a face model. If the system is able to use 32 images to create a model, the system will be able to "achieve [a] perfect realism and personalisation score," Samsung said.
Deep learning is increasingly capable of assessing the emotion of human faces, looking across an image to estimate how happy or sad the people in it appear to be. What if this could be applied to television news, estimating the average emotion of all of the human faces seen on the news over the course of a week? While AI-based facial sentiment assessment is still very much an active area of research, an experiment using Google's cloud AI to analyze a week's worth of television news coverage from the Internet Archive's Television News Archive demonstrates that even within the limitations of today's tools, there is a lot of visual sentiment in television news. To better understand the facial emotion of television, CNN, MSNBC and Fox News and the morning and evening broadcasts of San Francisco affiliates KGO (ABC), KPIX (CBS), KNTV (NBC) and KQED (PBS) from April 15 to April 22, 2019, totaling 812 hours of television news, were analyzed using Google's Vision AI image understanding API with all of its features enabled, including facial detection. Facial detection is very different from facial recognition.
San Francisco supervisors approved a ban on police using facial recognition technology, making it the first city in the U.S. with such a restriction. Amazon shareholders will continue selling the company's facial recognition technology "Rekognition" to governments and law enforcement agencies. During the e-commerce giant's annual meeting Wednesday, shareholders rejected all proposals including two related to Rekognition, Amazon confirmed to USA TODAY. One proposed banning the sales of the technology and the other called for the company to conduct an independent study and issue a report on the risks of governments using the technology. Amazon did not release shareholder vote totals Wednesday but said information would be filed with the U.S. Securities and Exchange Commission later in the week.
Amazon will continue to sell its controversial facial recognition software to law enforcement and other entities after its shareholders shot down a proposal to reel the technology in. The vote effectively kills two initiatives brought before Amazon's board. One proposal would have required board approval to sell the software to governments, with approval only being given if the client meets certain standards of civil liberties. Another proposal called for a study on the technology's implications on rights and privacy. The exact breakdown of the vote is unclear and according to an Amazon representative it will only be made available via SEC filings later this week.
Facial recognition cameras prevent crime, protect the public and do not breach the privacy of innocent people whose images are captured, a police force has argued. Ed Bridges, an office worker from Cardiff, claims South Wales police violated his privacy and data protection rights by using facial recognition technology on him. But Jeremy Johnson QC compared automated facial recognition (AFR) to the use of DNA to solve crimes and said it would have had little impact on Bridges. Johnson, representing the police, said: "AFR is a further technology that potentially has great utility for the prevention of crime, the apprehension of offenders and the protection of the public." The technology maps faces in a crowd and then compares them with a watch list of images, which can include suspects, missing people and persons of interest to the police.
The model is significantly faster to train and to make predictions, yet still requires a set of candidate regions to be proposed along with each input image. Python and C (Caffe) source code for Fast R-CNN as described in the paper was made available in a GitHub repository. The model architecture was further improved for both speed of training and detection by Shaoqing Ren, et al. at Microsoft Research in the 2016 paper titled "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." The architecture was the basis for the first-place results achieved on both the ILSVRC-2015 and MS COCO-2015 object recognition and detection competition tasks. The architecture was designed to both propose and refine region proposals as part of the training process, referred to as a Region Proposal Network, or RPN. These regions are then used in concert with a Fast R-CNN model in a single model design. These improvements both reduce the number of region proposals and accelerate the test-time operation of the model to near real-time with then state-of-the-art performance.