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Amazon facial recognition falsely matches more than 100 politicians to arrested criminals

The Independent - Tech

Amazon's controversial facial recognition technology has incorrectly matched more than 100 photos of politicians in the UK and US to police mugshots, new tests have revealed. Amazon Rekognition uses artificial intelligence software to identify individuals from their facial structure. Customers include law enforcement and US government agencies like Immigration and Custome Enforcement (ICE). It is not the first time the software's accuracy has been called into question. In July 2018, the American Civil Liberties Union (ACLU) found 28 false matches between US Congress members and pictures of people arrested for a crime.

Facebook settles facial recognition dispute


Facebook has settled a long-running legal dispute about the way it scans and tags people's photos. It will pay $550m (£421m) to a group of users in Illinois, who argued that its facial recognition tool was in violation of the state's privacy laws. The case has been ongoing since 2015, and the settlement was announced in its quarterly earnings. It comes as facial recognition use by the police, and in public spaces, comes under intense scrutiny. The lawsuit against Facebook was given the go-ahead in 2018 when a federal judge ruled it could be heard as a class action (group) case.

EmotionCues: AI Knows Whether Students Are Paying Attention


Facial recognition technology was introduced in the 1960s, languished through the AI winter, and in recent years has taken off -- boosted by increasingly powerful deep neural networks. Facial recognition has been applied in Face ID device unlocking functions, public security services, smart payment systems and more. During Taylor Swift's 2018 "Reputation" tour, the American singer-songwriter's security team utilized the tech to safeguard her from stalkers. Now, a research team from the Hong Kong University of Science and Technology and Harbin Engineering University has adopted facial recognition technology to analyze students' emotions in the classroom through a visual analytics system called "EmotionCues." Paper co-author Huamin Qu says the system "provides teachers with a quick and convenient measure of students' engagement level in a class. Knowing whether the lectures are too hard and when students get bored can help improve teaching."

Need India-specific data to properly implement facial recognition tech: Infosys co-founder


BENGALURU: As India plans to roll out a nationwide facial recognition system this year, Infosys co-founder Kris Gopalakrishnan believes that the country must develop its own databases for efficient implementation of breakthrough technologies that use artificial intelligence and machine learning. A facial recognition system is a technology capable of identifying or verifying a person by analysing patterns based on the person's facial textures and shape. Gopalakrishnan noted that India should carry out its own trials before implementing the facial recognition systems, as currently the algorithms used to train these mostly employ data of white men belonging to the Anglo-Saxon community, and it is unclear whether it will work properly in the country. "We also need to look at biases. One of the reasons why I believe India must do research in artificial intelligence (AI) and machine learning (ML) particularly is because most of the databases that are used to train these systems which we use today are being trained with data which is not from India," he told PTI in an interview on the sidelines of the Infosys Prize ceremony here.

Will China lead the world in AI by 2030?


China's huge population is helping the nation make great strides in facial-recognition technology.Credit: Gilles Sabrie/NYT/eyevine China not only has the world's largest population and looks set to become the largest economy -- it also wants to lead the world when it comes to artificial intelligence (AI). In 2017, the Communist Party of China set 2030 as the deadline for this ambitious AI goal, and, to get there, it laid out a bevy of milestones to reach by 2020. These include making significant contributions to fundamental research, being a favoured destination for the world's brightest talents and having an AI industry that rivals global leaders in the field. As this first deadline approaches, researchers note impressive leaps in the quality of China's AI research. They also predict a shift in the nation's ability to retain homegrown talent.

CyberLink CEO Dr. Jau Huang Shares Insights on Edge Computing and Showcases FaceMe AI-based Facial Recognition Engine at Intel Edge Computing Solution Summit - Business Wire - UrIoTNews


TAIPEI, Taiwan–(BUSINESS WIRE)–CyberLink Corp. (5203.TW), a pioneer of AI and facial recognition technologies, participated in the Intel Edge Computing Solution Summit. The summit brought together leaders from the IoT industry who shared insights on AI edge computing's latest breakthroughs and the opportunities that this technology will bring in the future. Dr. Jau Huang, CyberLink's founder and CEO, was invited to speak about the benefits of edge computing and how it enables precise, fast, affordable and secure AIoT use cases including facial recognition, such as the company's FaceMe AI-based engine. With FaceMe, CyberLink has leveraged edge-based technology and AI to deliver one of the world's most precise, flexible and best performing facial recognition engines. Compared with cloud-based solutions, edge computing is much cheaper, greatly enhances flexibility and provides real-time response, helping system integrators quickly develop and add new functionalities into existing systems and new AIoT products.

Datamorphic Testing: A Methodology for Testing AI Applications Artificial Intelligence

With the rapid growth of the applications of machine learning (ML) and other artificial intelligence (AI) techniques, adequate testing has become a necessity to ensure their quality. This paper identifies the characteristics of AI applications that distinguish them from traditional software, and analyses the main difficulties in applying existing testing methods. Based on this analysis, we propose a new method called datamorphic testing and illustrate the method with an example of testing face recognition applications. We also report an experiment with four real industrial application systems of face recognition to validate the proposed approach.

Text2FaceGAN: Face Generation from Fine Grained Textual Descriptions Machine Learning

--Powerful generative adversarial networks (GAN) have been developed to automatically synthesize realistic images from text. However, most existing tasks are limited to generating simple images such as flowers from captions. In this work, we extend this problem to the less addressed domain of face generation from fine-grained textual descriptions of face, e.g., "A person has curly hair, oval face, and mustache" . We are motivated by the potential of automated face generation to impact and assist critical tasks such as criminal face reconstruction. Since current datasets for the task are either very small or do not contain captions, we generate captions for images in the CelebA dataset by creating an algorithm to automatically convert a list of attributes to a set of captions. We then model the highly multi-modal problem of text to face generation as learning the conditional distribution of faces (conditioned on text) in same latent space. We utilize the current state-of-the-art GAN (DC-GAN with GAN-CLS loss) for learning conditional multi-modality. The presence of more fine-grained details and variable length of the captions makes the problem easier for a user but more difficult to handle compared to the other text-to-image tasks. We flipped the labels for real and fake images and added noise in discriminator . Generated images for diverse textual descriptions show promising results. In the end, we show how the widely used inceptions score is not a good metric to evaluate the performance of generative models used for synthesizing faces from text. I NTRODUCTION Photographic text-to-face synthesis is a mainstream problem with potential applications in image editing, video games, or for accessibility.

COMMENTARY: Limits to American Strategy to Blacklist Chinese AI Companies Coming Into View


If the U.S. blacklisting of China's budding artificial intelligence companies is aimed at thwarting their progress, we already are starting to see the limits to that strategy. A strong signal came this week when Megvii Technology, a Beijing-based AI firm on the U.S. blacklist, made moves to test the IPO waters in Hong Kong. Numerous media reports said the company, known for its facial-recognition platform Face, is seeking to raise $500 million to $1 billion in a listing that is imminent. The move follows the early success of Alibaba's (NYSE: BABA) secondary listing on the Hong Kong Stock Exchange that may raise as much as $13 billion next week. The HKSE was the world's top bourse for IPOs last year, supplanting Nasdaq, and looks primed to win that race again this year despite the civil unrest there.

Facebook scientists create video software to make people invisible to facial recognition technology

Daily Mail - Science & tech

Facebook has developed software to make people invisible to facial recognition technology. Its'de-identification' program is intended to protect people from'deepfake' style videos in which their faces can be edited onto videos of other people. These convincing clips are becoming so advanced it can be difficult to tell which videos are real and which ones are fake. And there are concerns that, in future, people will be able to make footage of others doing or saying things that they never actually did. But Facebook AI Research now says it has a way of fooling the artificial intelligence used to make these videos while still keeping the original video lifelike.