The Metropolitan police commissioner has hit back at demands to drop the use of live facial recognition cameras at this weekend's Notting Hill carnival over concerns of racial bias and an impending legal challenge. Mark Rowley wrote in a letter that the instant face-matching technology would be used at Europe's biggest street carnival "in a non-discriminatory way" using an algorithm that "does not perform in a way which exhibits bias". He was responding to a letter from 11 anti-racist and civil liberty organisations, disclosed in the Guardian, that urged the Met to scrap the use of the technology at an event that celebrates the African-Caribbean community. The Runnymede Trust, Liberty, Big Brother Watch, Race on the Agenda, and Human Rights Watch were among those who claimed in the letter to Rowley on Saturday that the technology "will only exacerbate concerns about abuses of state power and racial discrimination within your force". Campaigners claim the police have been allowed to "self-regulate" their use of the technology because of the lack of a legal framework and deploy the technology's algorithm at lower settings that are biased against ethnic minorities and women.

London (CNN Business)When you're in pain, you can usually tell someone about it. But for people with communication difficulties, that isn't always an option, meaning pain often goes undetected, misinterpreted or wrongly treated. To give a voice to those who can't report their suffering, such as people with dementia, PainChek, an Australian startup, has developed an app that uses facial analysis and artificial intelligence (AI) to assess and score pain levels. A carer records a short video of the subject's face using a smartphone and answers questions about their behavior, movements and speech. The app's AI recognizes facial muscle movements that are associated with pain and combines this with the carer's observations to calculate an overall pain score.

A retired police detective has claimed that Madeleine McCann could be found using facial recognition technology on Facebook. Madeleine vanished from the family's holiday apartment in Praia da Luz in Portugal on May 3, 2007, when she was three years old – with her mother Kate saying the 10th anniversary is a'horrible marker of time, stolen time'. Former Scotland Yard detective chief inspector Mick Neville has revealed that the site's cutting-edge software could help find Maddie, who would now be 14 years old. Mr Neville, a forensics expert, believes the state of the art technology could be used to trace Maddie because of a distinctive blemish in her right eye. He told The Sun: 'If she was still alive -- and there is no proof she is not -- then by using a combined tactic of technology and people with advanced facial recognition skills you could potentially find where Madeleine is today.'

The Department of Veterans Affairs will work with Flow Health to build a medical knowledge graph to inform decision-making and train artificial intelligence to personalize care plans. The objective of the 5-year partnership, Flow Health executives said in a statement, is to understand the common elements that make certain people susceptible to particular diseases, to pinpoint effective treatments and identify possible side effects in order to inform care decisions. VA and Flow Health's work will entail integrating large volumes of data in the quest to discover relationships between genomes and phenotypes.The goal is to learn what every gene variant means, to identify disease risk, to make more precise diagnoses and to suggest individualized treatments. "Our mission is to advance healthcare by applying the latest artificial intelligence techniques to improve the detection, diagnosis, treatment and management of diseases," Flow Health CEO Alex Meshkin said in a statement. Flow Health is building a knowledge graph of medicine and genomics comprising more than 30 petabytes of longitudinal clinical data drawn from VA records on 22 million veterans spanning more than 20 years.

A new learning model based on autoassociative neural networks is developped and applied to face detection. To extend the detection ability in orientation and to decrease the number of false alarms, different combinations of networks are tested: ensemble, conditional ensemble and conditional mixture of networks. The use of a conditional mixture of networks allows to obtain state of the art results on different benchmark face databases. The set of all possible windows is E V uN, with V n N 0. Since collecting a representative set of non-face examples is impossible, face detection by a statistical model is a difficult task. An autoassociative network, using five layers of neurons, is able to perform a nonlinear dimensionnality reduction [Kramer, 1991].

A new learning model based on autoassociative neural networks is developped and applied to face detection. To extend the detection ability in orientation and to decrease the number of false alarms, different combinations of networks are tested: ensemble, conditional ensemble and conditional mixture of networks. The use of a conditional mixture of networks allows to obtain state of the art results on different benchmark face databases. The set of all possible windows is E V uN, with V n N 0. Since collecting a representative set of non-face examples is impossible, face detection by a statistical model is a difficult task. An autoassociative network, using five layers of neurons, is able to perform a nonlinear dimensionnality reduction [Kramer, 1991].

To extend the detection abilityin orientation and to decrease the number of false alarms, different combinations of networks are tested: ensemble, conditional ensemble and conditional mixture of networks. The use of a conditional mixture of networks allows to obtain state of the art results on different benchmark face databases.