Facial recognition

In 2016 researchers at the University of Texas at Austin and Cornell University demonstrated that a neural network trained on image datasets can successfully identify faces and objects that have been blurred, pixellated, or obscured by the P3 privacy system. In some cases, the algorithm performed

In 2015, Facebook's AI lab announced that its researchers had devised an experimental algorithm that could recognise people in photographs even when their faces are hidden or turned away. The researchers trained a sophisticated neural network on a dataset of 40,000 photographs taken from Flickr

By 2016, numerous examples had surfaced of bias in facial recognition systems that meant they failed to recognise non-white faces, labelled non-white people as "gorillas", "animals", or "apes" (Google, Flickr), told Asian users their eyes were closed when taking photographs (Nikon), or tracked white

In September 2016, an algorithm assigned to pick the winners of a beauty contest examined selfies sent in by 600,000 entrants from India, China, the US, and all over Africa, and selected 44 finalists, almost all of whom were white. Of the six non-white finalists, all were Asian and only one had

In 2016, 21-year-old Russian photographer Egor Tsvetkov launched the "Your Face is Big Data" project. He created the project by semi-secretly photographing passengers seated across from him on the St. Petersburg metro, then uploading the images to an online service called FindFace. FindFace's

In 2015, a series of interviews with Moshe Greenshpan, the founder and CEO of the Israeli company Skakash, revealed the existence of the company's facial recognition software Churchix. The software is intended to help churches keep track of who attends services and other events by matching reference

In 2016, the Big Data lab at the Chinese search engine company Baidu published a study of an algorithm it had developed that it claimed could predict crowd formation and suggested it could be used to warn authorities and individuals of public safety threats stemming from unusually large crowds. The

A 2009 paper by the US National Academy of Sciences found that among forensic methods only DNA can reliably and consistency match evidence to specific individuals or sources. While it's commonly understood that techniques such as analysis of blood spatter patterns are up for debate, other types of

In 2017, an automated facial recognition dispenser was installed in one of the busiest toilets in Beijing in order to prevent theft of toilet paper rolls, chiefly by elderly residents. Would-be users must remove hats and glasses and stand in front of a high-definition camera for three seconds in

A US House of Representatives oversight committee was told in March 2017 that photographs of about half of the adult US population are stored in facial recognition databases that can be accessed by the FBI without their knowledge or consent. In addition, about 80% of the photos in the FBI's network

Few people realise how many databases may include images of their face; these may be owned by data brokers, social media companies such as Facebook and Snapchat, and governments. The systems in use by Snap and the Chinese start-up Face++ don't save facial images, but map detailed points on faces and

By 2017, facial recognition was developing quickly in China and was beginning to become embedded in payment and other systems. The Chinese startup Face++, valued at roughly $1 billion, supplies facial recognition software to Alipay, a mobile payment app used by more than 120 million people; the

In 2015 Hong Kong's Face of Litter campaign used DNA samples taken from street litter and collected from volunteers to create facial images that were then posted on billboards across the city. The campaign, conceived by PR firm Ogilvy & Mather and organised by online magazine Ecozine and the Nature